Tracking and Profiling Repeated Users Over Time in Text-Based Counseling: Longitudinal Observational Study With Hierarchical Clustering.

BACKGROUND: Due to their accessibility and anonymity, web-based counseling services are expanding at an unprecedented rate. One of the most prominent challenges such services face is repeated users, who represent a small fraction of total users but consume significant resources by continually returning to the system and reiterating the same narrative and issues. A deeper understanding of repeated users and tailoring interventions may help improve service efficiency and effectiveness. Previous studies on repeated users were mainly on telephone counseling, and the classification of repeated users tended to be arbitrary and failed to capture the heterogeneity in this group of users. OBJECTIVE: In this study, we aimed to develop a systematic method to profile repeated users and to understand what drives their use of the service. By doing so, we aimed to provide insight and practical implications that can inform the provision of service catering to different types of users and improve service effectiveness. METHODS: We extracted session data from 29,400 users from a free 24/7 web-based counseling service from 2018 to 2021. To systematically investigate the heterogeneity of repeated users, hierarchical clustering was used to classify the users based on 3 indicators of service use behaviors, including the duration of their user journey, use frequency, and intensity. We then compared the psychological profile of the identified subgroups including their suicide risks and primary concerns to gain insights into the factors driving their patterns of service use. RESULTS: Three clusters of repeated users with clear psychological profiles were detected: episodic, intermittent, and persistent-intensive users. Generally, compared with one-time users, repeated users showed higher suicide risks and more complicated backgrounds, including more severe presenting issues such as suicide or self-harm, bullying, and addictive behaviors. Higher frequency and intensity of service use were also associated with elevated suicide risk levels and a higher proportion of users citing mental disorders as their primary concerns. CONCLUSIONS: This study presents a systematic method of identifying and classifying repeated users in web-based counseling services. The proposed bottom-up clustering method identified 3 subgroups of repeated users with distinct service behaviors and psychological profiles. The findings can facilitate frontline personnel in delivering more efficient interventions and the proposed method can also be meaningful to a wider range of services in improving service provision, resource allocation, and service effectiveness.

Sophoricoside attenuates neuronal injury and altered cognitive function by regulating the LTR-4/NF-κB/PI3K signalling pathway in anaesthetic-exposed neonatal rats.

Introduction: This study examined the protective effects of sophoricoside on neuronal injury and cognitive dysfunction in anaesthetic-exposed neonatal rats. Material and methods: Neuronal injury was induced in rat pups by exposure to isoflurane (0.75%) with 30% oxygen for 6 h on P7. The protective effects of sophoricoside were evaluated by assessing cognitive function using the neurological score and Morris water maze. Neuronal apoptosis was assessed in hippocampus tissue using a TUNEL assay. The cytokine and macrophage inflammatory protein levels were assessed by ELISA. Western blot assays and RT-PCR were performed to assess the expression of NF-κB, TLR-4, Akt, and PI3K proteins in neuronal tissues. Immunohistochemical and histopathological changes were observed in the brain tissues of isoflurane-induced neuronal injury rats. Results: The sophoricoside treatment improved cognitive and neuronal function in rats exposed to isoflurane. Cytokine and MIP levels in the brain tissues of isoflurane-exposed rats decreased. However, sophoricoside treatment attenuated the expression of TLR-4, PI3K, and Akt protein in the brain tissues of isoflurane-exposed rats. The histopathology improved in the sophoricoside-treated isoflurane-exposed rats. Conclusions: Sophoricoside treatment protects against neuronal injury and reduced cognitive function in isoflurane-induced neuronal injury rats by regulating TLR-4 signalling.

Dynamic predictors of COVID-19 vaccination uptake and their interconnections over two years in Hong Kong.

The global rollout of COVID-19 vaccines faces a significant barrier in the form of vaccine hesitancy. This study adopts a dynamic and network perspective to explore the determinants of COVID-19 vaccine uptake in Hong Kong, focusing on multi-level determinants and their interconnections. Following the framework proposed by the Strategic Advisory Group of Experts (SAGE), the study used repeated cross-sectional surveys to map these determinants at multiple levels and investigates their interconnections simultaneously in a sample of 15,179 over two years. The results highlight the dynamic nature of COVID-19 vaccine hesitancy in an evolving pandemic. The findings suggest that vaccine confidence attitudes play crucial roles in vaccination uptake, with their importance shifting over time. The initial emphasis on vaccine safety gradually transitioned to heightened consideration of vaccine effectiveness at a later stage. The study also highlights the impact of chronic condition, age, COVID-19 case numbers, and non-pharmaceutical preventive behaviours on vaccine uptake. Higher educational attainment and being married were associated with primary and booster vaccine uptake and it may be possible to leverage these groups as early innovation adopters. Trust in government acts as a crucial bridging factor linking various variables in the networks with vaccine confidence attitudes, which subsequently closely linked to vaccine uptake. This study provides insights for designing future effective vaccination programmes for changing circumstances.

Evaluating the effectiveness of concurrent sessions and counselors' attention allocation in online counseling.

OBJECTIVE: With its anonymity and accessibility, text-based online counseling has shown great potential in reaching people with mental health needs. One strategy adopted to meet the service gap is concurrent counseling, that is, each counselor attending to more than one client at a time. To date, there is no reported evidence supporting its rationality and effectiveness. This study investigated the potential opportunities, effectiveness, and caveats in concurrent service delivery and identified the optimal cutoff number of concurrent sessions while maintaining the quality of service at or above a set threshold. METHOD: We analyzed the transcript of 54,716 online counseling sessions from Open Up, a free, 24/7 text-based counseling service, to develop an attention score that measures the attention allocation of counselors and examined the impact of the counselor's attention allocation on client satisfaction and service outcomes. RESULTS: On average, compared to nonconcurrent sessions, concurrent sessions were longer, more likely to end prematurely, and had lower client satisfaction. We also identified an optimal attention score of approximately 0.4 (out of 1.0, which denotes full attention), which translates to two to three concurrent sessions. CONCLUSIONS: This study provides empirical evidence for the feasibility of conducting multiple text-based sessions concurrently without compromising service quality and client experience. Our method of measuring the counselor attention allocation offers a way to systematically assess and evaluate concurrent sessions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Developing and validating a parser-based suicidality detection model in text-based mental health services.

BACKGROUND: Advances in text-mining can potentially aid online text-based mental health services in detecting suicidality. However, false positive remains a challenge. METHODS: Data of a free 24/7 online text-based counseling service in Hong Kong were used to develop a novel parser-based algorithm (PBSD) to detect suicidal ideation while minimizing false alarms. Sessions containing keywords related to suicidality were extracted (N = 1267). PBSD first applies a sentence parser to work out the grammatical structure of each sentence, including subject, object, dependent and modifier. Then a set of syntax rules were applied to judge if a flagged sentence is a true or false positive. Half of the sessions were randomly selected to train PBSD, the remaining were used as the test set. A standard keywords matching model was adopted as the baseline comparison. Accuracy and recall were reported to demonstrate models' performance. RESULTS: Of the 1267 sessions, 585 (46.2 %) were false alarms. The false alarms were categorized into four types: negation-induced false alarms (NIFA; 14 %), subject-induced false alarms (SIFA; 19 %), tense-induced false alarms (TIFA; 30 %), and other types of false alarms (OTFA; 37 %). PBSD significantly outperforms the baseline keywords matching model on accuracy (0.68 vs 0.53, 28.3 %). It successfully amended 36.8 % (105/297) lexicon matching-caused false alarms. The reduction on recall was marginal (1 vs 0.96, 4 %). CONCLUSIONS: The proposed model significantly improves the use of lexicon-based method by reducing false alarms and improving the accuracy of suicidality detection. It can potentially reduce unnecessary panic and distraction caused by false alarms among frontline service-providers.

Variability in the psychological impact of four waves of COVID-19: a time-series study of 60 000 text-based counseling sessions.

BACKGROUND: Continuous exposure to stressors can lead to vulnerability and, in some cases, resilience. This study examined the variation in its psychological impact across the first four waves of COVID-19 in Hong Kong. METHODS: Transcripts from Open Up, an online text-based counseling service, between January 2019 and January 2021 were analyzed (N = 60 775). We identified COVID-19 mentioned sessions using keywords and further categorized them into those that also mentioned symptoms of common mental disorders (CMDs) and those that did not. Autoregressive integrated moving average models were used to analyze the associations between the severity of the outbreak and the mention of COVID-19 and CMDs. RESULTS: Results revealed that the pandemic led to increased psychological distress. Compared to prior to its advent, more people sought help in the initial months of the outbreak. Furthermore, associations were found between the severity of the outbreak and the number of help-seeker mentioning the pandemic, as well as between the outbreak severity and the number of help-seekers disclosing psychological distress. However, these relationships were not uniform across the four waves of outbreaks; a dissociation between outbreak severity and help-seekers' concern was found in the fourth wave. CONCLUSION: As the pandemic waxes and wanes, people may become habituated to its psychological toll. This may be interpreted as a form of resilience. Instead of worsening with time, the psychological impact of COVID-19 may reduce with repeated exposure.

Network-based prediction of the disclosure of ideation about self-harm and suicide in online counseling sessions.

BACKGROUND: In psychological services, the transition to the disclosure of ideation about self-harm and suicide (ISS) is a critical point warranting attention. This study developed and tested a succinct descriptor to predict such transitions in an online synchronous text-based counseling service. METHOD: We analyzed two years' worth of counseling sessions (N = 49,770) from Open Up, a 24/7 service in Hong Kong. Sessions from Year 1 (N = 20,618) were used to construct a word affinity network (WAN), which depicts the semantic relationships between words. Sessions from Year 2 (N = 29,152), including 1168 with explicit ISS, were used to train and test the downstream ISS prediction model. We divided and classified these sessions into ISS blocks (ISSBs), blocks prior to ISSBs (PISSBs), and non-ISS blocks (NISSBs). To detect PISSB, we adopted complex network approaches to examine the distance among different types of blocks in WAN. RESULTS: Our analyses find that words within a block tend to form a module in WAN and that network-based distance between modules is a reliable indicator of PISSB. The proposed model yields a c-statistic of 0.79 in identifying PISSB. CONCLUSIONS: This simple yet robust network-based model could accurately predict the transition point of suicidal ideation prior to its explicit disclosure. It can potentially improve the preparedness and efficiency of help-providers in text-based counseling services for mitigating self-harm and suicide.

In online counseling, the help-provider can often be engaging with several service users simultaneously. Therefore, new tools that could help to alert and assist the help-provider and increase their preparedness for getting further help for service users could be useful. In this study, we developed and tested a new tool that is designed to alert help-providers to the disclosure of self-harm and suicidal thoughts, based on the words that the service user has been typing. The tool is developed on the basis that word usage may have a specific pattern when suicidal thoughts are more likely to occur. We tested our tool using two years' worth of online counseling conversations and we show that our approach can help to predict the confession of suicidal thoughts. As such, we are taking a step forward in helping to improve these counseling services.

Social Media Sentiments on Suicides at the New York City Landmark, Vessel: A Twitter Study.

Vessel is a landmark created by Heatherwick Studio where visitors can enjoy views of New York City from different heights and perspectives. However, between February 2020 and July 2021, four individuals jumped to their deaths from the landmark. Effective preventive solutions have yet to be identified, and the site is currently closed. In this study, we examined the trajectory of public sentiment on the suicide-related activity at Vessel on Twitter by investigating the engagement patterns and identifying themes about the four suicides from February 2020 to August 2021 (n = 3058 tweets). The results show increased levels of discussion about each successive suicide case in the first 14 days following each incident (from 6 daily tweets for the first case to 104 for the fourth case). It also took longer for relevant discussions to dissipate (4 days for the first and 14 days for the fourth case, KS statistic = 0.71, p < 0.001). Thematic analysis shows a shift from expressions of emotion to urging suicide prevention actions in the third and fourth cases; additionally, we detected growing support for restricting means. We suggest that, prior to the reopening of Vessel, collective efforts should be made to install safety protections and reduce further suicide risks.

Ultrasound-guided quadratus lumborum block provided more effective analgesia for children undergoing lower abdominal laparoscopic surgery: a randomized clinical trial.

BACKGROUND: Postoperative pain treatment for pediatrics is often inadequate and the evidence of pediatric postoperative analgesia is scarce. To our knowledge, no report regarding the comparison among caudal block, transversus abdominis plane (TAP) block and quadratus lumborum (QL) block for children undergoing lower abdominal laparoscopic surgery was found at present. Thus this trial aimed to compare the efficacies of them for children undergoing lower abdominal laparoscopic surgery. METHODS: One hundred and eighty children aged from 1 to 12 years undergoing lower abdominal laparoscopic surgery were included and randomized to receive caudal block, TAP block or QL block. The primary outcome was the Face, Legs, Activity, Cry, and Consolability (FLACC) score at 30 min, 1 h, 4 h, 8 h, 12 h, and 24 h and tramadol consumption during first 24 h postoperatively. Secondary outcomes included the number of children received tramadol, time to first tramadol request, parents' satisfaction and postoperative adverse reactions. RESULTS: The QLB group had lower postoperative FLACC scores at 8 h (median difference - 0.43, P = 0.03) than the Caudal group and at 4 h (median difference - 0.6, P = 0.001) and 8 h (median difference - 0.43, P = 0.03) than the TAPB group. The tramadol consumption was lower in the QLB group (28.43 ± 6.55) than the TAPB group (37.17 ± 6.12, P = 0.023). Although the number of children received tramadol did not differ among the three groups, the time to first tramadol request was longer in the QLB group (7.20 ± 0.79) than the caudal group (8.42 ± 0.61, P = 0.008). No statistical difference was observed concerning other secondary outcomes. CONCLUSIONS: QLB produced more effective postoperative analgesia for children undergoing laparoscopic abdominal surgery compared with the TAPB and caudal block.

Sevoflurane Offers Neuroprotection in a Cerebral Ischemia/Reperfusion Injury Rat Model Through the E2F1/EZH2/TIMP2 Regulatory Axis.

Cerebral ischemia/reperfusion (I/R) injury contributes considerably to the poor prognosis in patients with ischemic stroke. This study is aimed to delineate the molecular mechanistic actions by which sevoflurane protects against cerebral I/R injury. A rat model of cerebral I/R injury was established and pre-treated with sevoflurane, in which hippocampal neuron apoptosis was found to be repressed and the level of E2F transcription factor 1 (E2F1) was observed to be down-regulated. Then, the up-regulated expression of E2F1 was validated in rats with cerebral I/R injury, responsible for stimulated neuron apoptosis. Further, the binding of E2F1 to enhancer of zeste homolog 2 (EZH2) and EZH2 to tissue inhibitor of metalloproteinases-2 (TIMP2) was identified. The stimulative effect of the E2F1/EZH2/TIMP2 regulatory axis on neuron apoptosis was subsequently demonstrated through functional assays. After that, it was substantiated in vivo that sevoflurane suppressed the apoptosis of hippocampal neurons in rats with cerebral I/R injury by down-regulating E2F1 to activate the EZH2/TIMP2 axis. Taken together, our data elucidated that sevoflurane reduced neuron apoptosis through mediating the E2F1/EZH2/TIMP2 regulatory axis, thus protecting rats against cerebral I/R injury.

Detecting premature departure in online text-based counseling using logic-based pattern matching.

BACKGROUND: More so than face-to-face counseling, users of online text-based services might drop out from a session before establishing a clear closure or expressing the intention to leave. Such premature departure may be indicative of heightened risk or dissatisfaction with the service or counselor. However, there is no systematic way to identify this understudied phenomenon. PURPOSE: This study has two objectives. First, we developed a set of rules and used logic-based pattern matching techniques to systematically identify premature departures in an online text-based counseling service. Second, we validated the importance of premature departure by examining its association with user satisfaction. We hypothesized that the users who rated the session as less helpful were more likely to have departed prematurely. METHOD: We developed and tested a classification model using a sample of 575 human-annotated sessions from an online text-based counseling platform. We used 80% of the dataset to train and develop the model and 20% of the dataset to evaluate the model performance. We further applied the model to the full dataset (34,821 sessions). We compared user satisfaction between premature departure and completed sessions based on data from a post-session survey. RESULTS: The resulting model achieved 97% and 92% F1 score in detecting premature departure cases in the training and test sets, respectively, suggesting it is highly consistent with the judgment of human coders. When applied to the full dataset, the model classified 15,150 (43.5%) sessions as premature departure and the remaining 19,671 (56.5%) as completed sessions. Completed cases (15.2%) were more likely to fill the post-chat survey than premature departure cases (4.0%). Premature departure was significantly associated with lower perceived helpfulness and effectiveness in distress reduction. CONCLUSIONS: The model is the first that systematically and accurately identifies premature departure in online text-based counseling. It can be readily modified and transferred to other contexts for the purpose of risk mitigation and service evaluation and improvement.

The Opportunities and Challenges of the First Three Years of Open Up, an Online Text-Based Counselling Service for Youth and Young Adults.

We present the opportunities and challenges of Open Up, a free, 24/7 online text-based counselling service to support youth in Hong Kong. The number of youths served more than doubled within the first three years since its inception in 2018 in response to increasing youth suicidality and mental health needs. Good practice models are being developed in order to sustain and further scale up the service. We discuss the structure of the operation, usage pattern and its effectiveness, the use of AI to improve users experience, and the role of volunteer in the operation. We also present the challenges in further enhancing the operation, calling for more research, especially on the identification of the optimal number of users that can be concurrently served by a counsellor, the effective approach to respond to a small percentage of repeated users who has taken up a disproportional volume of service, and the way to optimize the use of big data analytics and AI technology to enhance the service. These advancements will benefit not only Open Up but also similar services across the globe.

Detecting suicide risk using knowledge-aware natural language processing and counseling service data.

RATIONALE: Detecting users at risk of suicide in text-based counseling services is essential to ensure that at-risk individuals are flagged and prioritized. OBJECTIVE: The objective of this study is to develop a domain knowledge-aware risk assessment (KARA) model to improve our ability of suicide detection in online counseling systems. METHODS: We obtained the largest known de-identified dataset from an emotional support system established in Hong Kong, comprising 5682 Cantonese conversations between help-seekers and counselors. Of those, 682 conversations disclosed crisis intentions of suicide. We constructed a suicide-knowledge graph, representing suicide-related domain knowledge as a computer-processible graph. Such knowledge graph was embedded into a deep learning model to improve its ability to identify help-seekers in crisis. As the baseline, a standard NLP model was applied to the same task. 80% of the study samples were randomly sampled to train model parameters. The remaining 20% were used for model validation. Evaluation metrics including precision, recall, and c-statistic were reported. RESULTS: Both KARA and the baseline achieved high precision (0.984 and 0.951, shown in Table 2) and high recall (0.942 and 0.947) towards non-crisis cases. For crisis cases, however, KARA model achieved a much higher recall than the baseline (0.870 vs 0.791). The c-statistics of KARA and the baseline were 0.815 and 0.760, respectively. CONCLUSION: KARA significantly outperformed standard NLP models, demonstrating good translational value and clinical relevance.

Complete chloroplast genome sequence determination of Rheum species and comparative chloroplast genomics for the members of Rumiceae.

KEY MESSAGE: Complete plastomes of Rheum species facilitated to clarify the phylogeny of Polygonaceae, and comparative chloroplast genomics contributed to develop genetic markers for the authentication of Rheum species. Rheum (Polygonaceae) is widely distributed throughout the temperate and subtropical areas of Asian interior. Rheum species are usually perennial herbs, and half of them are endemic to China with important medicinal properties. On account of similar morphological characteristics, species delimitation of Rheum still remains unclear. Chloroplast genomes of eight Rheum species, Rumex crispus and Oxyria digyna were characterized. Based on the comparison of genome structure of these species and the two published Rheum species, it is shown that plastome sequences of these species are relatively conserved with the same gene order, and three Sect. Palmata species remarkably showed high sequence similarities. Some hotspots could be used to discriminate the Rheum species, and 17 plastid genes were subject to positive selection. The phylogenetic analyses indicated that all the Polygonaceae species were clustered in the same group and showed that Rheum species, except for Rheum wittrockii, formed a monophyletic group with high maximum parsimony/maximum likelihood bootstrap support values and Bayesian posterior probabilities. The molecular dating based on plastomes indicated that the divergences within Polygonaceae species were dated to the Upper Cretaceous period [73.86-77.99 million years ago (Ma)]. The divergence of Sect. Palmata species was estimated to have occurred around 1.60 Ma, indicating that its diversification was affected by the repeated climatic fluctuation in the Quaternary.

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection.

The aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage IV colorectal cancer patients. Four basic ML algorithms were used for prediction-logistic regression, decision tree, GradientBoosting and lightGBM. The research samples were randomly divided into a training group and a testing group at a ratio of 8:2. 999 patients with stage 4 colorectal cancer were included in this study. In the training group, the GradientBoosting model's AUC value was the highest, at 0.881. The Logistic model's AUC value was the lowest, at 0.734. The GradientBoosting model had the highest F1_score (0.912). In the test group, the AUC Logistic model had the lowest AUC value (0.692). The GradientBoosting model's AUC value was 0.734, which can still predict cancer progress. However, the gbm model had the highest AUC value (0.761), and the gbm model had the highest F1_score (0.974). The GradientBoosting model and the gbm model performed better than the other two algorithms. The weight matrix diagram of the GradientBoosting algorithm shows that chemotherapy, age, LogCEA, CEA and anesthesia time were the five most influential risk factors for tumor recurrence. The four machine learning algorithms can each predict the risk of tumor recurrence in patients with stage IV colorectal cancer after surgery. Among them, GradientBoosting and gbm performed best. Moreover, the GradientBoosting weight matrix shows that the five most influential variables accounting for postoperative tumor recurrence are chemotherapy, age, LogCEA, CEA and anesthesia time.

Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes.

OBJECTIVE: To use machine learning algorithms to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge. METHODS: 1570 stage I-III breast cancer patients receiving treatment from Sun Yat-sen Memorial Hospital were analyzed. Machine learning was used to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge. RESULTS: The results showed that platelets, LMR (lymphocyte-to-monocyte ratio), age, PLR (the platelet-to-lymphocyte ratio) and white blood cell counts accounted for a significant weight in the 5-year prognosis of triple-negative breast cancer patients. The results of model prediction indicated that rankings for accuracy among the training group (from high to low) were forest, gbm, and DecisionTree (0.770335, 0.760766, 0.751994, 0.737640 and 0.734450, respectively). For AUC value (high to low), they were forest, Logistic and DecisionTree (0.896673, 0.895408, 0.776836, 0.722799 and 0.702804, respectively). The highest MSE value for DecisionTree was 0.2656, and the lowest MSE value for forest was 0.2297. In the test group, accuracy rankings (from high to low) were DecisionTree, and GradientBoosting (0.748408, 0.738854, 0.738854, 0.732484 and gbm, respectively). For AUC value (high to low), the rankings were GradientBoosting, gbm, and DecisionTree (0.731595, 0.715438, 0.712767, 0.708348 and 0.691960, respectively). The maximum MSE value for gbm was 0.2707, and the minimum MSE value for DecisionTree was 0.2516. CONCLUSION: The machine learning algorithm can predict the death outcomes of patients with triple-negative breast cancer 5 years after discharge. This can be used to estimate individual outcomes for patients with triple-negative breast cancer.

The Complete Chloroplast Genome of Euphrasia regelii, Pseudogenization of ndh Genes and the Phylogenetic Relationships Within Orobanchaceae.

Euphrasia (Orobanchaceae) is a genus which is widely distributed in temperate regions of the southern and northern hemisphere. The taxonomy of Euphrasia is still controversial due to the similarity of morphological characters and a lack of genomic resources. Here, we present the first complete chloroplast (cp) genome of this taxonomically challenging genus. The cp genome of Euphrasia regelii consists of 153,026 bp, including a large single-copy region (83,893 bp), a small single-copy region (15,801 bp) and two inverted repeats (26,666 bp). There are 105 unique genes, including 71 protein-coding genes, 30 tRNA and 4 rRNA genes. Although the structure and gene order is comparable to the one in other angiosperm cp genomes, genes encoding the NAD(P)H dehydrogenase complex are widely pseudogenized due to mutations resulting in frameshifts, and stop codon positions. We detected 36 dispersed repeats, 7 tandem repeats and 65 simple sequence repeat loci in the E. regelii plastome. Comparative analyses indicated that the cp genome of E. regelii is more conserved compared to other hemiparasitic taxa in the Pedicularideae and Buchnereae. No structural rearrangements or loss of genes were detected. Our analyses suggested that three genes (clpP, ycf2 and rps14) were under positive selection and other genes under purifying selection. Phylogenetic analysis of monophyletic Orobanchaceae based on 45 plastomes indicated a close relationship between E. regelii and Neobartsia inaequalis. In addition, autotrophic lineages occupied the earliest diverging branches in our phylogeny, suggesting that autotrophy is the ancestral trait in this parasitic family.

Application of Ultrasound-Guided Ilioinguinal/Iliohypogastric Nerve Block in Pediatric Same-Day Surgery.

The aim of this study was to evaluate the safety and efficacy of ultrasound-guided ilioinguinal/iliohypogastric nerve block (IINB) in pediatric patients undergoing same-day inguinal region surgery. Ninety patients aged 4-6 years, ASA levels I-II, were randomly divided into three groups: U, T, or C (n = 30 each). After basic anesthesia, patients in group U underwent ultrasound-guided IINB, those in group T underwent traditional Schulte-Steinberg IINB, and those in group C (controls) received intravenous anesthesia (ketamine-propofol) only. Patients who remained sensitive to intraoperative stimuli received additional intravenous doses of 1 mg/kg ketamine. Heart rate (HR), mean arterial pressure (MAP), and oxygen saturation (SPO2) were recorded upon entering the operating room (T0), at skin incision (T1), while pulling the hernia sac (T2), during skin closing (T3), and upon awakening (T4) at recovery. HR and MAP at T1, T2, and T4 were higher in group C than those in the other two groups, and recovery time in group C was significantly prolonged (P < 0.05). Group U required significantly lower quantities and frequency of ketamine injection, and pain scores in group U during awakening were lower than those in the other two groups (P < 0.05). Ultrasound-guided IINB provided an improved nerve block effect and postoperative analgesia, reduced the amount of local anesthetic required, facilitated more rapid postoperative recovery, and was a safe and effective method of anesthesia.