A male germ-cell-specific ribosome controls male fertility.

Ribosomes are highly sophisticated translation machines that have been demonstrated to be heterogeneous in the regulation of protein synthesis1,2. Male germ cell development involves complex translational regulation during sperm formation3. However, it remains unclear whether translation during sperm formation is performed by a specific ribosome. Here we report a ribosome with a specialized nascent polypeptide exit tunnel, RibosomeST, that is assembled with the male germ-cell-specific protein RPL39L, the paralogue of core ribosome (RibosomeCore) protein RPL39. Deletion of RibosomeST in mice causes defective sperm formation, resulting in substantially reduced fertility. Our comparison of single-particle cryo-electron microscopy structures of ribosomes from mouse kidneys and testes indicates that RibosomeST features a ribosomal polypeptide exit tunnel of distinct size and charge states compared with RibosomeCore. RibosomeST predominantly cotranslationally regulates the folding of a subset of male germ-cell-specific proteins that are essential for the formation of sperm. Moreover, we found that specialized functions of RibosomeST were not replaceable by RibosomeCore. Taken together, identification of this sperm-specific ribosome should greatly expand our understanding of ribosome function and tissue-specific regulation of protein expression pattern in mammals.

Language Use in Conversational Agent-Based Health Communication: Systematic Review.

BACKGROUND: Given the growing significance of conversational agents (CAs), researchers have conducted a plethora of relevant studies on various technology- and usability-oriented issues. However, few investigations focus on language use in CA-based health communication to examine its influence on the user perception of CAs and their role in delivering health care services. OBJECTIVE: This review aims to present the language use of CAs in health care to identify the achievements made and breakthroughs to be realized to inform researchers and more specifically CA designers. METHODS: This review was conducted by following the protocols of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. We first designed the search strategy according to the research aim and then performed the keyword searches in PubMed and ProQuest databases for retrieving relevant publications (n=179). Subsequently, 3 researchers screened and reviewed the publications independently to select studies meeting the predefined selection criteria. Finally, we synthesized and analyzed the eligible articles (N=11) through thematic synthesis. RESULTS: Among the 11 included publications, 6 deal exclusively with the language use of the CAs studied, and the remaining 5 are only partly related to this topic. The language use of the CAs in these studies can be roughly classified into six themes: (1) personal pronouns, (2) responses to health and lifestyle prompts, (3) strategic wording and rich linguistic resources, (4) a 3-staged conversation framework, (5) human-like well-manipulated conversations, and (6) symbols and images coupled with phrases. These derived themes effectively engaged users in health communication. Meanwhile, we identified substantial room for improvement based on the inconsistent responses of some CAs and their inability to present large volumes of information on safety-critical health and lifestyle prompts. CONCLUSIONS: This is the first systematic review of language use in CA-based health communication. The results and limitations identified in the 11 included papers can give fresh insights into the design and development, popularization, and research of CA applications. This review can provide practical implications for incorporating positive language use into the design of health CAs and improving their effective language output in health communication. In this way, upgraded CAs will be more capable of handling various health problems particularly in the context of nationwide and even worldwide public health crises.

Exosomal miR-27 negatively regulates ROS production and promotes granulosa cells apoptosis by targeting SPRY2 in OHSS.

Ovarian hyperstimulation syndrome (OHSS) is one of the most dangerous iatrogenic complications in controlled ovarian hyperstimulation (COH). The exact molecular mechanism that induces OHSS remains unclear. In recent years, accumulating evidence found that exosomal miRNAs participate in many diseases of reproductive system. However, the specific role of miRNAs, particularly the follicular fluid-derived exosomal miRNAs in OHSS remains controversial. To identify differentially expressed follicular fluid exosomal miRNAs from OHSS and non-OHSS patients, the analysis based on miRNA-sequence was conducted. The levels of 291 miRNAs were significantly differed in exosomes from OHSS patients compared with normal control, and exosomal miR-27 was one of the most significantly down-regulated miRNAs in the OHSS group. By using MiR-27 mimic, we found it could increase ROS stress and apoptosis by down-regulating the expression of p-ERK/Nrf2 pathway by negatively regulating SPRY2. These data demonstrate that exosomal miRNAs are differentially expressed in follicular fluid between patients with and without OHSS, and follicular fluid exosomal miR-27 may involve in the pathological process of OHSS development.

TNF-α - mediated peripheral and central inflammation are associated with increased incidence of PND in acute postoperative pain.

BACKGROUND: Acute postoperative pain plays an important role in the perioperative neurocognitive disorders (PND). The pathogenesis of PND is still unknown, but it is generally believed that peripheral and central nervous system inflammation play an important role, and acute postoperative pain is also thought to aggravate postoperative inflammatory response. The aim of the present study is to explore the effect of acute postoperative pain on peripheral and central nervous system inflammation and related cognitive impairment behaviour in elderly rats after surgery. METHODS: Rats were assigned into four groups: control, surgery for internal fixation for tibial fracture, surgery with analgesia using intraperitoneal morphine, and morphine without surgery. Pain was assessed by the Subjective Pain Scale. The spatial memory of rats was assessed by the Morris water maze (delayed matching task) from the second day to the seventh day after surgery (POD2-POD7). In part of the rats, the pro-inflammatory cytokines TNF-α in plasma, the medial prefrontal cortex (mPFC), and the hippocampus were determined by ELISA on the POD2. The activation of microglia and the expression of c-Fos in the hippocampal CA1 regions and mPFC were detected by the immunohistochemical method on the POD2. RESULTS: Acute postoperative pain and spatial memory impairment occurred after operation, and postoperative analgesia could significantly improve the both parameters. Additionally, on the POD2, the levels of TNF-α in plasma, hippocampus and mPFC were significantly increased, while the activation of microglia cells and the expression c-Fos in the hippocampal CA1 regions and mPFC were significantly increased. And postoperative analgesia with morphine significantly inhibited the above reactions. CONCLUSION: Our data suggest that acute postoperative pain increases the incidence of perioperative neurocognitive disorders. Peripheral and central nervous system inflammation may be involved in this cognitive impairment. And reducing the intensity of acute postoperative pain may be one of the main preventive strategies for PND.

The effect of transversus abdominis plane block on the chronic pain after colorectal surgery: a retrospective cohort study.

BACKGROUND: Chronic postsurgical pain (CPSP) is common and would reduce the quality of life of patients. Transversus abdominal plane (TAP) block has been widely used in lower abdominal surgery and many researches demonstrated that it could improve acute postsurgical pain. We aim to determine whether TAP block could improve chronic postoperative pain at 3 months and 6 months after colorectal surgery. METHODS: A total of 307 patients received selective colorectal surgery under general anesthesia between January, 2015 and January, 2019 in a single university hospital were included: 128 patients received TAP block combined with patient-controlled intravenous analgesia (PCIA) for postsurgical analgesia (group TP) and 179 only administrated with PCIA (group P). Main outcome was the NRS score of pain at 3 months after colorectal surgery. The data was analyzed by two-way repeated measures anova and the chi-square test. RESULTS: The NRS score at rest and during movement was decreased significantly at 24 h after surgery (rest NRS 1.07 ± 1.34 vs 1.65 ± 1.67, movement NRS 3.00 ± 1.45 vs 3.65 ± 1.89; all P = 0.003) in group TP than those of group P. There was no significant difference of NRS score at 48 h after surgery (P > 0.05). At 3 months after surgery, the NRS score during movement was also lower in group TP than that in group P (0.59 ± 1.23 vs 0.92 ± 1.65, P = 0.045). There was no significant difference of NRS score at 6 months after surgery (P > 0.05). The prevalence of CPSP was 19.5% (25/128) in group TP and 20.7% (37/179) in group P at 3 months after surgery. 13.2% (17/128) of patients suffered from CPSP in group TP and 13.9% (25/179) in group P at 6 months after surgery. Both at 3 months and 6 months after surgery, there was no statistical difference of the prevalence of CPSP between the two groups (all P > 0.05) . CONCLUSIONS: TAP block reduced NRS during movement at 3 months after surgery but did not reduce the incidence of CPSP at 3 months and 6 months after selective colorectal surgery.

Risk Factor Analysis and Intervention Study for Unspecific Functional Enteropathy in a Maritime Environment: A Cross-Sectional Analytical Survey.

Background: The study aimed to investigate the risk factors and interventions for unspecific functional bowel disorders (U-FBDs) in military personnel under maritime environment. Methods: This cross-sectional analytical survey used the Rome III questionnaire for surveying 1018 military personnel involved in overseas humanitarian medical services from June 2013 to January 2016. Individuals diagnosed with U-FBDs were included in the U-FBDs group, while those without FBDs or other diseases were considered the control group. The psychological and sleep conditions of military personnel with U-FBDs were assessed using the SCL-90 scale and the Pittsburgh Sleep Quality Index scale, respectively. Health education and treatment were provided to individuals diagnosed with U-FBDs, and the improvements were evaluated after three months. Results: Among 923 qualified questionnaires, 243 subjects was included in U-FBDs group and 240 in the control group. Smoking, alcohol consumption, and multiple seafaring missions were identified as risk factors for U-FBDs in military personnel on ocean-going missions. The U-FBDs group had significantly worse sleep quality, sleep efficiency, daytime dysfunction score, and total PSQI score compared to the control group (P < 0.05). Additionally, 10 factor scores of SCL-90 and the total score in the U-FBDs group were significantly higher than those in the control group (P < 0.01). Patients with U-FBDs also reported the highest rate of somatic symptoms (P < 0.01). Conclusion: The onset of U-FBDs among military personnel on long-haul maritime may be closely related to mental, psychological, and sleep factors. Health education and treatment may help improve the symptoms of U-FBDs.

The optimal dose of oxycodone in PCIA after laparoscopic surgery for gastrointestinal cancer in elderly patients: A randomized controlled trial.

Objective: To explore the optimal bolus dose of oxycodone for patient controlled intravenous analgesia (PCIA) without background dose in elderly patients after laparoscopic surgery for gastrointestinal cancer. Methods: In this prospective, randomized, double-blind, parallel-controlled study, we recruited patients aged 65 years or older. They underwent laparoscopic resection for gastrointestinal cancer and received PCIA after surgery. Eligible patients were randomly divided into 0.01, 0.02, or 0.03 mg/kg group according to the bolus dose of oxycodone in PCIA. The primary outcome was VAS scores of pain on mobilization at 48 h after surgery. Secondary endpoints included the VAS scores of rest pain, the total and effective numbers of press in PCIA, cumulative dose of oxycodone used in PCIA, the incidence of nausea, vomiting and dizziness, as well as patients' satisfaction at 48 h after surgery. Results: A total of 166 patients were recruited and randomly assigned to receive a bolus dose of 0.01 mg/kg (n = 55), 0.02 mg/kg (n = 56) or 0.03 mg/kg (n = 55) of oxycodone in PCIA. The VAS scores of pain on mobilization, the total and effective numbers of press in PCIA in 0.02 mg/kg group and 0.03 mg/kg group were lower than those in 0.01 mg/kg group (P < 0.05). Cumulative dose of oxycodone used in PCIA and patients' satisfaction in 0.02 and 0.03 mg/kg groups were more than those in 0.01 mg/kg group (P < 0.01). The incidence of dizziness in 0.01 and 0.02 mg/kg groups was lower than that in 0.03 mg/kg group (P < 0.01). There were no significant differences in VAS scores of rest pain, the incidence of nausea and vomiting among three groups (P > 0.05). Conclusion: For elderly patients undergoing laparoscopic surgery for gastrointestinal cancer, 0.02 mg/kg bolus dose of oxycodone in PCIA without background infusion may be a better choice.

c-Jun as a one-way valve at the naive to primed interface.

BACKGROUND: c-Jun is a proto-oncogene functioning as a transcription factor to activate gene expression under many physiological and pathological conditions, particularly in somatic cells. However, its role in early embryonic development remains unknown. RESULTS: Here, we show that c-Jun acts as a one-way valve to preserve the primed state and impair reversion to the naïve state. c-Jun is induced during the naive to primed transition, and it works to stabilize the chromatin structure and inhibit the reverse transition. Loss of c-Jun has surprisingly little effect on the naïve to primed transition, and no phenotypic effect on primed cells, however, in primed cells the loss of c-Jun leads to a failure to correctly close naïve-specific enhancers. When the primed cells are induced to reprogram to a naïve state, these enhancers are more rapidly activated when c-Jun is lost or impaired, and the conversion is more efficient. CONCLUSIONS: The results of this study indicate that c-Jun can function as a chromatin stabilizer in primed EpiSCs, to maintain the epigenetic cell type state and act as a one-way valve for cell fate conversions.

Expression of Y-Box-binding protein 1 in Chinese patients with breast cancer.

The purpose of this study was to investigate the expression of Y-Box-binding protein 1 (YB-1) in breast cancer and its correlation with clinicopathological characteristics and prognosis. Paraffin sections were retrospectively collected from 239 cases of stage I-III breast cancer patients and 30 healthy females who received surgery between January 2000 and December 2004 in the Chinese People's Liberation Army General Hospital. The protein expression of YB-1 was detected by immunohistochemistry. The expression difference between the two groups and the correlation between YB-1 expression and clinicopathological characteristics and breast cancer prognosis were analyzed. Within the breast cancer group, YB-1 was expressed in the cytoplasm in 100.0% (239/239) of cases and in the nucleus in 36.8% (88/239) of cases. Within the control group of normal breast tissue, YB-1 was expressed in the cytoplasm in 100.0% (30/30) of cases and in the nucleus in 16.7% (5/30) of cases. The expression of YB-1 in the nucleus of breast cancer cells was significantly higher than that in normal breast tissue (P = 0.029). The expression of YB-1 in the nucleus of breast cancer cells positively correlated with the Scarff-Bloom-Richardson grade (P = 0.007) and HER-2 expression (P = 0.005), negatively correlated with ER expression (P = 0.004), and was independent of the age, menstrual status, pathological type, tumor size, lymph node status, presence of thrombosis, PR expression, and EGFR expression. The 5-year disease-free survival (DFS) and overall survival (OS) of patients with positive YB-1 expression in the nucleus were significantly lower than those of patients who were negative for nuclear YB-1 expression, and the difference was statistically significant (DFS 65.9% vs. 82.1%, P = 0.000; OS 79.5% vs. 92.1%, P = 0.000). Multivariate analysis suggested that the expression of YB-1 in the nucleus is an independent prognostic factor that affects DFS and OS in breast cancer patients (DFS P = 0.015; OS P = 0.035). In conclusion, the expression of YB-1 in the nucleus is related to carcinogenesis and the development of breast cancer. Therefore, YB-1 is an important molecular marker that can be used to predict breast cancer prognosis.

[Effects of single versus combined use of trastuzumab and cantide on breast cancer cells SKBR3 over-expressing HER2].

OBJECTIVE: To evaluate the in vitro cytotoxic effects of cantide or herceptin on human breast cancer SKBR3 cells over-expressing HER2. METHODS: The distribution of HER2 and hTERT protein in SKBR3 cells and the effects of cantide and/or herceptin on the subcellular localization of HER2 and hTERT were observed by indirect immunofluorescent assay. The inhibition rate of herceptin and/or cantide at different concentrations on SKBR3 cells was detected by MTT assay. And the apoptotic rate of cells was evaluated by flow cytometer. RESULTS: (1) The expressions of both HER2 and hTERT proteins in SKBR3 cells were found. HER2 protein was predominant in cell membranes while hTERT protein in nuclei. After the addition of herceptin, the cytoplasmic migration of HER2 was found while there was no distinct location change of cantide. (2) In MTT assay, the single use of cantide or herceptin and the combined use of both produced inhibitory effects on SKBR3 cells while the inhibition rate was higher for combined use. The inhibitory effects became additive in the combined use of 0.4 µmol/L cantide and 0.85 µg/ml herceptin. And there were synergistic effects in the combined use of 0.4 µmol/L cantide and 1.70, 3.40, 6.88 or 13.75 µg/ml herceptin. (3) The apoptotic rate was 25.75% for cantide alone, 11.26% for herceptin alone and 41.41% for their combined use (apoptotic cells predominant in advanced stage). CONCLUSION: Due to different localizations, cantide and herceptin have different action sites in their combined use. When in single use, the inhibition rate is linearly correlated with the concentration of herceptin or cantide. And their combined use produces additive or synergistic antitumor effects on SKBR3 breast cancer cells.

Use of Health Care Chatbots Among Young People in China During the Omicron Wave of COVID-19: Evaluation of the User Experience of and Satisfaction With the Technology.

BACKGROUND: Long before the outbreak of COVID-19, chatbots had been playing an increasingly crucial role and gaining growing popularity in health care. In the current omicron waves of this pandemic when the most resilient health care systems at the time are increasingly being overburdened, these conversational agents (CA) are being resorted to as preferred alternatives for health care information. For many people, especially adolescents and the middle-aged, mobile phones are the most favored source of information. As a result of this, it is more important than ever to investigate the user experience of and satisfaction with chatbots on mobile phones. OBJECTIVE: The objective of this study was twofold: (1) Informed by Deneche and Warren's evaluation framework, Zhu et al's measures of variables, and the theory of consumption values (TCV), we designed a new assessment model for evaluating the user experience of and satisfaction with chatbots on mobile phones, and (2) we aimed to validate the newly developed model and use it to gain an understanding of the user experience of and satisfaction with popular health care chatbots that are available for use by young people aged 17-35 years in southeast China in self-diagnosis and for acquiring information about COVID-19 and virus variants that are currently spreading. METHODS: First, to assess user experience and satisfaction, we established an assessment model based on relevant literature and TCV. Second, the chatbots were prescreened and selected for investigation. Subsequently, 413 informants were recruited from Nantong University, China. This was followed by a questionnaire survey soliciting the participants' experience of and satisfaction with the selected health care chatbots via wenjuanxing, an online questionnaire survey platform. Finally, quantitative and qualitative analyses were conducted to find the informants' perception. RESULTS: The data collected were highly reliable (Cronbach α=.986) and valid: communalities=0.632-0.823, Kaiser-Meyer-Olkin (KMO)=0.980, and percentage of cumulative variance (rotated)=75.257% (P<.001). The findings of this study suggest a considerable positive impact of functional, epistemic, emotional, social, and conditional values on the participants' overall user experience and satisfaction and a positive correlation between these values and user experience and satisfaction (Pearson correlation P<.001). The functional values (mean 1.762, SD 0.630) and epistemic values (mean 1.834, SD 0.654) of the selected chatbots were relatively more important contributors to the students' positive experience and overall satisfaction than the emotional values (mean 1.993, SD 0.683), conditional values (mean 1.995, SD 0.718), and social values (mean 1.998, SD 0.696). All the participants (n=413, 100%) had a positive experience and were thus satisfied with the selected health care chatbots. The 5 grade categories of participants showed different degrees of user experience and satisfaction: Seniors (mean 1.853, SD 0.108) were the most receptive to health care chatbots for COVID-19 self-diagnosis and information, and second-year graduate candidates (mean 2.069, SD 0.133) were the least receptive; freshmen (mean 1.883, SD 0.114) and juniors (mean 1.925, SD 0.087) felt slightly more positive than sophomores (mean 1.989, SD 0.092) and first-year graduate candidates (mean 1.992, SD 0.116) when engaged in conversations with the chatbots. In addition, female informants (mean 1.931, SD 0.098) showed a relatively more receptive attitude toward the selected chatbots than male respondents (mean 1.999, SD 0.051). CONCLUSIONS: This study investigated the use of health care chatbots among young people (aged 17-35 years) in China, focusing on their user experience and satisfaction examined through an assessment framework. The findings show that the 5 domains in the new assessment model all have a positive impact on the participants' user experience and satisfaction. In this paper, we examined the usability of health care chatbots as well as actual chatbots used for other purposes, enriching the literature on the subject. This study also provides practical implication for designers and developers as well as for governments of all countries, especially in the critical period of the omicron waves of COVID-19 and other future public health crises.

Chinese Version of the Mobile Health App Usability Questionnaire: Translation, Adaptation, and Validation Study.

BACKGROUND: The usability of mobile health (mHealth) apps needs to be effectively evaluated before they are officially approved to be used to deliver health interventions. To this end, the mHealth App Usability Questionnaire (MAUQ) has been designed and proved valid and reliable in assessing the usability of mHealth apps. However, this English questionnaire needs to be translated into other languages, adapted, and validated before being utilized to evaluate the usability of mHealth apps. OBJECTIVE: This study aims to improve, further adapt, and validate the Chinese version of the MAUQ (C-MAUQ; interactive for patients) on Left-handed Doctor, one of the most popular "reaching out to patients" interactive mHealth apps with chatbot function in China, to test the reliability and cross-cultural adaptability of the questionnaire. METHODS: The MAUQ (interactive for patients) has been translated into Chinese and validated for its reliability on Good Doctor, one of the most influential "reaching out to patients" mHealth apps without chatbot function in China. After asking for the researchers' approval to use this Chinese version, we adjusted and further adapted the C-MAUQ by checking it against the original English version and improving its comprehensibility, readability, idiomaticity, and cross-cultural adaptability. Following a trial survey completed by 50 respondents on wenjuanxing, the most popular online questionnaire platform in China, the improved version of the C-MAUQ (I-C-MAUQ) was finally used to evaluate the usability of Left-handed Doctor through an online questionnaire survey (answered by 322 participants) on wenjuanxing, to test its internal consistency, reliability, and validity. RESULTS: The I-C-MAUQ still retained the 21 items and 3 dimensions of the original MAUQ: 8 items for usability and satisfaction, 6 items for system information arrangement, and 7 items for efficiency. The translation problems in the C-MAUQ, including (1) redundancy, (2) incompleteness, (3) misuse of parts of speech, (4) choice of inappropriate words, (5) incomprehensibility, and (6) cultural difference-induced improper translation, were improved. As shown in the analysis of data obtained through the online survey, the I-C-MAUQ had a better internal consistency (ie, the correlation coefficient between the score of each item and the total score of the questionnaire determined within the range of 0.861-0.938; P<.01), reliability (Cronbach α=.988), and validity (Kaiser-Meyer-Olkin=0.973), compared with the C-MAUQ. It was effectively used to test the usability of Left-handed Doctor, eliciting over 80% of informants' positive attitudes toward this mHealth app. CONCLUSIONS: The I-C-MAUQ is highly reliable and valid for Left-handed Doctor, and suitable for testing the usability of interactive mHealth apps used by patients in China. This finding further confirms the cross-cultural validity, reliability, and adaptability of the MAUQ. We identified certain factors influencing the perceived usability of mHealth apps, including users' age, gender, education, profession, and possibly previous experience with mHealth apps and the chatbot function of such apps. Most notably, we found a wider acceptance of this new technology among young Chinese female college students who were more engaged in the interaction with health care chatbots. The age-, gender-, and profession-induced preference for new digital health interventions in China aligns with the findings in other similar studies in America and Malaysia. This preference identifies areas for further research on the social, cultural, and gender adaptation of health technologies.

Interventions in Chinese Undergraduate Students' Mental Health: Systematic Review.

BACKGROUND: Over 30% of university students from 8 countries were afflicted with mental distress according to a World Health Organization survey. Undergraduate students in increasing numbers in China have also been reported to suffer from different mental problems. Various psychological distresses significantly impact their academic and daily life, thereby causing role impairments and unsatisfactory academic achievements. While the prevalence of, diverse underlying factors for, and interventions of social support in college students' mental health have extensively been investigated in China, there is no study exclusively focusing on the impact of interventions on their psychological well-being. OBJECTIVE: The aim of this review was to identify and synthesize the interventions in the mental health concerns of Chinese undergraduate students studying in China reported in the literature to inform educational authorities, college and university management, students' affairs counselors, and mental health providers. METHODS: We performed a systematic review and reported the research findings of previous studies according to the protocol of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. First, based on the predefined search strategy, keyword searches were performed in the PubMed and ProQuest databases to retrieve relevant studies. Subsequently, we screened the candidate articles based on predefined inclusion and exclusion criteria. Finally, we analyzed the included papers for qualitative synthesis. RESULTS: We retrieved a total of 675 studies from the PubMed and ProQuest databases using the search strategy on March 15, 2022. Among these candidate studies, 15 that were not written in English, 76 duplicates, and 149 studies of other document types were removed before screening. An additional 313 studies were excluded in the screening process, with 73 articles ruled out for being not relevant to interventions, not related to mental health, or not focused on undergraduate students in the full-text review. As a result, 49 papers were eligible and included in this systematic review. In the qualitative synthesis, we divided the interventions reported in the selected studies into two categories: (1) social support from government authorities, university authorities, students' affairs counselors and teachers, family members, health care authorities and professionals, and the media (various online platforms), and (2) various coping strategies adopted by undergraduate students themselves. We identified further research on mental health interventions that may be delivered by digital medical platforms, conversational agents (eg, chatbots), and researchers. CONCLUSIONS: This was the first systematic review of interventions to address the mental health concerns of Chinese undergraduate students studying in China. The categorization of reported interventions and the identification of new intervention channels can effectively inform stakeholders. Interventions for undergraduate students' mental health is a research topic worth further investigation.

Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis.

BACKGROUND: Mental disorders (MDs) impose heavy burdens on health care (HC) systems and affect a growing number of people worldwide. The use of mobile health (mHealth) apps empowered by artificial intelligence (AI) is increasingly being resorted to as a possible solution. OBJECTIVE: This study adopted a topic modeling (TM) approach to investigate the public trust in AI apps in mental health care (MHC) by identifying the dominant topics and themes in user reviews of the 8 most relevant mental health (MH) apps with the largest numbers of reviewers. METHODS: We searched Google Play for the top MH apps with the largest numbers of reviewers, from which we selected the most relevant apps. Subsequently, we extracted data from user reviews posted from January 1, 2020, to April 2, 2022. After cleaning the extracted data using the Python text processing tool spaCy, we ascertained the optimal number of topics, drawing on the coherence scores and used latent Dirichlet allocation (LDA) TM to generate the most salient topics and related terms. We then classified the ascertained topics into different theme categories by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand the status of public trust in AI apps in MHC. RESULTS: From the top 20 MH apps with the largest numbers of reviewers retrieved, we chose the 8 (40%) most relevant apps: (1) Wysa: Anxiety Therapy Chatbot; (2) Youper Therapy; (3) MindDoc: Your Companion; (4) TalkLife for Anxiety, Depression & Stress; (5) 7 Cups: Online Therapy for Mental Health & Anxiety; (6) BetterHelp-Therapy; (7) Sanvello; and (8) InnerHour. These apps provided 14.2% (n=559), 11.0% (n=431), 13.7% (n=538), 8.8% (n=356), 14.1% (n=554), 11.9% (n=468), 9.2% (n=362), and 16.9% (n=663) of the collected 3931 reviews, respectively. The 4 dominant topics were topic 4 (cheering people up; n=1069, 27%), topic 3 (calming people down; n=1029, 26%), topic 2 (helping figure out the inner world; n=963, 25%), and topic 1 (being an alternative or complement to a therapist; n=870, 22%). Based on topic coherence and intertopic distance, topics 3 and 4 were combined into theme 3 (dispelling negative emotions), while topics 2 and 1 remained 2 separate themes: theme 2 (helping figure out the inner world) and theme 1 (being an alternative or complement to a therapist), respectively. These themes and topics, though involving some dissenting voices, reflected an overall high status of trust in AI apps. CONCLUSIONS: This is the first study to investigate the public trust in AI apps in MHC from the perspective of user reviews using the TM technique. The automatic text analysis and complementary manual interpretation of the collected data allowed us to discover the dominant topics hidden in a data set and categorize these topics into different themes to reveal an overall high degree of public trust. The dissenting voices from users, though only a few, can serve as indicators for health providers and app developers to jointly improve these apps, which will ultimately facilitate the treatment of prevalent MDs and alleviate the overburdened HC systems worldwide.

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers.

Background: Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. Objective: To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. Methods: All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. Results: The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini-Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: "dobj_stdev" (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202-1.838, P < 0.001), "cl_av_deps" (dependents per clause) (OR, 1.597, 95% CI, 1.202-2.122, P, 0.001), and "VP_T" (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211-4.104, P, 0.010). Finally, we illustrated the clinical use of the classifier with Bayes' monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. Conclusion: Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence.

COVID-19-Related Health Inequalities Induced by the Use of Social Media: Systematic Review.

Background: COVID-19-related health inequalities were reported in some studies, showing the failure in public health and communication. Studies investigating the contexts and causes of these inequalities pointed to the contribution of communication inequality or poor health literacy and information access to engagement with health care services. However, no study exclusively dealt with health inequalities induced by the use of social media during COVID-19. Objective: This review aimed to identify and summarize COVID-19-related health inequalities induced by the use of social media and the associated contributing factors and to characterize the relationship between the use of social media and health disparities during the COVID-19 pandemic. Methods: A systematic review was conducted on this topic in light of the protocol of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. Keyword searches were performed to collect papers relevant to this topic in multiple databases: PubMed (which includes MEDLINE [Ovid] and other subdatabases), ProQuest (which includes APA PsycINFO, Biological Science Collection, and others), ACM Digital Library, and Web of Science, without any year restriction. Of the 670 retrieved publications, 10 were initially selected based on the predefined selection criteria. These 10 articles were then subjected to quality analysis before being analyzed in the final synthesis and discussion. Results: Of the 10 articles, 1 was further removed for not meeting the quality assessment criteria. Finally, 9 articles were found to be eligible and selected for this review. We derived the characteristics of these studies in terms of publication years, journals, study locations, locations of study participants, study design, sample size, participant characteristics, and potential risk of bias, and the main results of these studies in terms of the types of social media, social media use-induced health inequalities, associated factors, and proposed resolutions. On the basis of the thematic synthesis of these extracted data, we derived 4 analytic themes, namely health information inaccessibility-induced health inequalities and proposed resolutions, misinformation-induced health inequalities and proposed resolutions, disproportionate attention to COVID-19 information and proposed resolutions, and higher odds of social media-induced psychological distress and proposed resolutions. Conclusions: This paper was the first systematic review on this topic. Our findings highlighted the great value of studying the COVID-19-related health knowledge gap, the digital technology-induced unequal distribution of health information, and the resulting health inequalities, thereby providing empirical evidence for understanding the relationship between social media use and health inequalities in the context of COVID-19 and suggesting practical solutions to such disparities. Researchers, social media, health practitioners, and policy makers can draw on these findings to promote health equality while minimizing social media use-induced health inequalities.

Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features.

BACKGROUND: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. OBJECTIVE: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. METHODS: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). RESULTS: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR-) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR- (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. CONCLUSION: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.

Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals.

Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these principles provide valuable benchmarks to assess the quality of health resources developed by local health authorities. In this paper, we developed machine learning classifiers for health professionals with or without Chinese proficiency to assess public-oriented health information in Chinese based on the definition of effective health communication by the WHO. We compared our optimized classifier (SVM_F5) with the state-of-art Chinese readability classifier (Chinese Readability Index Explorer CRIE 3.0), and classifiers adapted from established English readability formula, Gunning Fog Index, Automated Readability Index. Our optimized classifier achieved statistically significant higher area under the receiver operator curve (AUC of ROC), accuracy, sensitivity, and specificity than those of SVM using CRIE 3.0 features and SVM using linguistic features of Gunning Fog Index and Automated Readability Index (ARI). The statistically improved performance of our optimized classifier compared to that of SVM classifiers adapted from popular readability formula suggests that evaluation of health communication effectiveness as defined by the principles of the WHO is more complex than information readability assessment. Our SVM classifier validated on health information covering diverse topics (environmental health, infectious diseases, pregnancy, maternity care, non-communicable diseases, tobacco control) can aid effectively in the automatic assessment of original, translated Chinese public health information of whether they satisfy or not the current international standard of effective health communication as set by the WHO.

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers.

We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental health information of high actionability for people with mental disorders. sensitivity of the classifier using both semantic and structural features as variables achieved statistically higher than that of the binary classifier using either semantic (p < 0.001) or structural features (p = 0.0010). The specificity of the optimized classifier was statistically higher than that of the classifier using structural variables (p = 0.002) and the classifier using semantic variables (p = 0.001). Differences in specificity between the full-variable classifier and the optimised classifier were statistically insignificant (p = 0.687). These findings suggest the optimised classifier using as few as 19 semantic-structural variables was the best-performing classifier. By combining insights of linguistics and statistical analyses, we effectively increased the interpretability and the diagnostic utility of the binary classifiers to guide the development, evaluation of the actionability and usability of mental healthcare information.

Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation.

BACKGROUND: Machine translation (MT) technologies have increasing applications in healthcare. Despite their convenience, cost-effectiveness, and constantly improved accuracy, research shows that the use of MT tools in medical or healthcare settings poses risks to vulnerable populations. OBJECTIVES: We aimed to develop machine learning classifiers (MNB and RVM) to forecast nuanced yet significant MT errors of clinical symptoms in Chinese neural MT outputs. METHODS: We screened human translations of MSD Manuals for information on self-diagnosis of infectious diseases and produced their matching neural MT outputs for subsequent pairwise quality assessment by trained bilingual health researchers. Different feature optimisation and normalisation techniques were used to identify the best feature set. RESULTS: The RVM classifier using optimised, normalised (L2 normalisation) semantic features achieved the highest sensitivity, specificity, AUC, and accuracy. MNB achieved similar high performance using the same optimised semantic feature set. The best probability threshold of the best performing RVM classifier was found at 0.6, with a very high positive likelihood ratio (LR+) of 27.82 (95% CI: 3.99, 193.76), and a low negative likelihood ratio (LR-) of 0.19 (95% CI: 0.08, 046), suggesting the high diagnostic utility of our model to predict the probabilities of erroneous MT of disease symptoms to help reverse potential inaccurate self-diagnosis of diseases among vulnerable people without adequate medical knowledge or an ability to ascertain the reliability of MT outputs. CONCLUSION: Our study demonstrated the viability, flexibility, and efficiency of introducing machine learning models to help promote risk-aware use of MT technologies to achieve optimal, safer digital health outcomes for vulnerable people.