The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up.

The prevalence of osteoporosis has drastically increased recently. It is not only the most frequent but is also a major global public health problem due to its high morbidity. There are many risk factors associated with osteoporosis were identified. However, most studies have used the traditional multiple linear regression (MLR) to explore their relationships. Recently, machine learning (Mach-L) has become a new modality for data analysis because it enables machine to learn from past data or experiences without being explicitly programmed and could capture nonlinear relationships better. These methods have the potential to outperform conventional MLR in disease prediction. In the present study, we enrolled a Chinese post-menopause cohort followed up for 4 years. The difference of T-score (δ-T score) was the dependent variable. Information such as demographic, biochemistry and life styles were the independent variables. Our goals were: (1) Compare the prediction accuracy between Mach-L and traditional MLR for δ-T score. (2) Rank the importance of risk factors (independent variables) for prediction of δ T-score. Totally, there were 1698 postmenopausal women were enrolled from MJ Health Database. Four different Mach-L methods namely, Random forest (RF), eXtreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and stochastic gradient boosting (SGB), to construct predictive models for predicting δ-BMD after four years follow-up. The dataset was then randomly divided into an 80% training dataset for model building and a 20% testing dataset for model testing. A 10-fold cross-validation technique for hyperparameter tuning was used. The model with the lowest root mean square error for the validation dataset was viewed as the best model for each ML method. The averaged metrics of the RF, SGB, NB, and XGBoost models were used to compare the model performance of the benchmark MLR model that used the same training and testing dataset as the Mach-L methods. We defined that the priority demonstrated in each model ranked 1 as the most critical risk factor and 22 as the last selected risk factor. For Pearson correlation, age, education, BMI, HDL-C, and TSH were positively and plasma calcium level, and baseline T-score were negatively correlated with δ-T score. All four Mach-L methods yielded lower prediction errors than the MLR method and were all convincing Mach-L models. From our results, it could be noted that education level is the most important factor for δ-T Score, followed by DBP, smoking, SBP, UA, age, and LDL-C. All four Mach-L outperformed traditional MLR. By using Mach-L, the most important six risk factors were selected which are, from the most important to the least: DBP, SBP, UA, education level, TG and sleeping hour. δ T score was positively related to SBP, education level, UA and TG and negatively related to DBP and sleeping hour in postmenopausal Chinese women.

Health technology assessment and innovation: here to help or hinder?

Innovative health technologies offer much to patients, clinicians, and health systems. Policy makers can, however, be slow to embrace innovation for many reasons, including a less robust body of evidence, perceived high costs, and a fear that once technologies enter the health system, they will be difficult to remove. Health technology funding decisions are usually made after a rigorous health technology assessment (HTA) process, including a cost analysis. However, by focusing on therapeutic value and cost-savings, the traditional HTA framework often fails to capture innovation in the assessment process. How HTA defines, evaluates, and values innovation is currently inconsistent, and it is generally agreed that by explicitly defining innovation would recognize and reward and, in turn, stimulate, encourage, and incentivize future innovation in the system. To foster innovation in health technology, policy needs to be innovative and utilize other HTA tools to inform decision making including horizon scanning, multicriteria decision analysis, and funding mechanisms such as managed agreements and coverage with evidence development. When properly supported and incentivized, and by shifting the focus from cost to investment, innovation in health technology such as genomics, point-of-care testing, and digital health may deliver better patient outcomes. Industry and agency members of the Health Technology Assessment International Asia Policy Forum (APF) met in Taiwan in November 2023 to discuss the potential of HTA to foster innovation, especially in the Asia region. Discussions and presentations during the 2023 APF were informed by a background paper, which forms the basis of this paper.

Annonaceous acetogenins mimic AA005 targets mitochondrial trifunctional enzyme alpha subunit to treat obesity in male mice.

Obesity and related diseases pose a major health risk, yet current anti-obesity drugs inadequately addressing clinical needs. Here we show AA005, an annonaceous acetogenin mimic, resists obesity induced by high-fat diets and leptin mutations at non-toxic doses, with the alpha subunit of the mitochondrial trifunctional protein (HADHA) as a target identified through proteomics and in vitro validation. Pharmacokinetic analysis shows AA005 enriches in adipose tissue, prompting the creation of adipose-specific Hadha-deficient mice. These mice significantly mitigate diet-induced obesity, echoing AA005's anti-obesity effects. AA005 treatment and Hadha deletion in adipose tissues increase body temperature and energy expenditure in high-fat diet-fed mice. The beneficial impact of AA005 on obesity mitigation is ineffective without uncoupling protein 1 (UCP1), essential for thermogenesis regulation. Our investigation shows the interaction between AA005 and HADHA in mitochondria, activating the UCP1-mediated thermogenic pathway. This substantiates AA005 as a promising compound for obesity treatment, targeting HADHA specifically.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study.

BACKGROUND: Epidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent. OBJECTIVES: We used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period. METHODS: Ninety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors. RESULTS: After adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0-1 day (0.31, 95 % confidence interval [CI]: 0.24-0.38), 0-7 days (0.68, 95 % CI: 0.57-0.79), and 0-50 days (1.02, 95 % CI: 0.82-1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0-1 day (0.36, 95 % CI: 0.25-0.47), 0-7 days (0.62, 95 % CI: 0.46-0.77), and 0-50 days (1.27, 95 % CI: 0.87-1.67) lag periods. Over a 0-7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (ß = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (ß = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (ß = 0.85 vs. 0.45; P_int = 0.028), diabetics (ß = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (ß = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0-50 day lag, males (ß = 0.99 vs. 0.96; P_int < 0.0001), diabetics (ß = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (ß = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (ß = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses. CONCLUSIONS: Prolonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.

INTRODUCTION: The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D. METHODS: We recruited older T2D between 60-95 years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost). RESULTS: Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance. CONCLUSION: In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.

Incidence, Risk Factors and Management of Postoperative Complications in Horizontal Strabismus Surgery.

OBJECTIVE: To report the incidence, risk factors and management of postoperative complications after horizontal strabismus surgery. DESIGN: Retrospective Cohort study. PARTICIPANTS: The study assessed 1,273 patients with 1,035 cases of exotropia and 238 cases of esotropia, with a minimum 18-month follow-up. METHODS: Retrospective review of strabismus operation patients' medical records included baseline demographics, age at surgery, pre/postoperative visual acuity, and deviation. Complications were categorized as surgical site (infection, scarring, cyst, granuloma, ischemia) and strabismus-related (recurrence, diplopia), with analysis of incidence, risk factors, and management. RESULTS: Among surgical site complications, the incidence of infection, pyogenic granuloma, and anterior segment ischemia were similar between the exotropia (0.3%, 0.3%, 0.2%) and esotropia (0.8%, 0%, 0.4%) groups (p = .221, 0.406, 0.515). In contrast, the esotropia group presented a higher risk of conjunctival inclusion cyst and conjunctival scar than the exotropia group, with incidences of 5.0% vs 2.2% and 6.3% vs 1.3%, respectively (p = .004, <0.001). Regarding strabismus complications, the incidence of early recurrence was not significant between the two groups, with 10.0% in the exotropia group and 10.5% in the esotropia group (p = .553). Older age and poor initial visual acuity were associated with early recurrence (p < .001). The esotropia group had a higher risk of persistent diplopia than the exotropia group, with incidences of 4.2% vs 2.0%, respectively (p = .003). CONCLUSION: Esotropia carries a higher risk of conjunctival inclusion cysts, conjunctival scarring, and persistent diplopia compared to the exotropia group, while both groups exhibit similar rates of early recurrence and other surgical site complications.

Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.

BACKGROUND: The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships. AIM: To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS. METHODS: 556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods. RESULTS: Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS. CONCLUSION: Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

Ethical and legal considerations in social media research for health technology assessment: conclusions from a scoping review.

OBJECTIVES: The objective was to identify and describe the published guidance and current academic discourse of ethical issues and standards related to the use of Social Media Research for generating patient insights for the use by health technology assessment (HTA) or health policy decisions. METHODS: A scoping review of the literature was conducted in PubMed and Embase and identified 935 potential references published between January 2017 and June 2021. After title and abstract screening by three reviewers, 40 publications were included, the relevant information was extracted and data were collected in a mind map, which was then used to structure the output of the review. RESULTS: Social Media Research may reveal new insights of relevance to HTA or health policies into patient needs, patient experiences, or patient behaviors. However, the research approaches, methods, data use, interpretation, and communication may expose those who post the data in social media channels to risks and potential harms relating to privacy, anonymity/confidentiality, authenticity, context, and rapidly changing technologies. CONCLUSIONS: An actively engaged approach to ensuring ethical innocuousness is recommended that carefully follows best practices throughout planning, conduct, and communication of the research. Throughout the process and as a follow-up, there should be a discourse with the ethical experts to maximally protect the current and future users of social media, to support their trust in the research, and to advance the knowledge in parallel to the advancement of the media themselves, the technologies, and the research tools.

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.

Using social media research in health technology assessment: stakeholder perspectives and scoping review.

OBJECTIVES: The aim of this initiative was to examine collaboratively, in a multi-stakeholder team (health technology assessment (HTA) practitioners with patient involvement expertise, health technology industry, patient advocates, health policy experts, patient engagement experts), whether evidence generated through social media research (SMR) fills current information gaps relating to insights on specific aspects of patient experiences, preferences, or patient needs and delivers additional value to HTA. METHODS: The framing of the project was done in a co-creative, deliberative multi-stakeholder process. Challenge and refinement happened through discussions with 25 independent stakeholders from HTA bodies, industry, academia, and patient advocacy. For critical themes identified during the framing phase, scoping literature reviews were performed including the state of methods and examples for the use of SMR in HTA. RESULTS: The framing and stakeholder discussions specified a set of expectations and requirements, and the scoping reviews revealed the current state of methods and usage of SMR in health-policy decision making. CONCLUSIONS: The project concluded that SMR can contribute new, relevant evidence to HTA. It is however recommended to evolve the science through defining best practices when planning, conducting, and using SMR and to conduct multi-stakeholder pilot SMR projects to address questions relevant to current HTAs and to validate and improve the proposed practices.

[Determination of three plant growth regulators in Dendrobium officinale and Anoectochilus roxburghii by three-phase hollow fiber liquid phase microextraction- high performance liquid chromatography].

Dendrobium officinale (D. officinale) and Anoectochilus roxburghii (A. roxburghii) are precious raw materials for traditional Chinese medicine. The growing demand for D. officinale and A. roxburghii cannot be met by current production techniques. Hence, the widespread artificial cultivation of D. officinale and A. roxburghii using substantial amounts of plant growth regulators (PGRs) has emerged. The excessive use of PGRs not only affects the quality and efficacy of medicinal materials but also causes a series of safety issues. Therefore, expanding research on residual PGRs in valuable Chinese medicinal materials is important to avoid the health hazards caused by these substances. Unfortunately, the identification of PGRs is challenging because of their trace and complex matrices. High performance liquid chromatography (HPLC) has become one of the mainstream analytical methods for PGR determination. An important consideration in the application of this technique to the detection of trace acidic PGRs is how to improve its accuracy and sensitivity. Three-phase hollow fiber liquid phase microextraction (3P-HF-LPME) has the advantages of a high enrichment factor, complex sample purification ability, low reagent consumption, low cost, and easy integration with chromatographic systems. Thus, the 3P-HF-LPME method overcomes the many shortcomings of traditional sample pretreatment methods. In this study, a novel, simple, and effective analytical method based on 3P-HF-LPME combined with HPLC was developed to extract, purify, enrich, and detect three trace acidic PGRs (indole-3-acetic acid, naphthyl acetic acid and indolebutyric acid) in D. officinale and A. roxburghii. The chromatographic separation conditions and 3P-HF-LPME model parameters were systematically optimized for this purpose. First, the sample solution was prepared by ultrasonication and low-temperature standing, and then adjusted to pH 3.0 using dilute hydrochloric acid. The sample solution (10 mL) and NaCl (1.50 g) were stored in a 15 mL brown extraction bottle with a built-in magnetic stirrer. Next, 30 µL of NaOH solution (pH 11.0) as the inner phase solution was injected into the inner cavity of a hollow fiber tube, which was subsequently sealed at both ends. The hollow fiber tube was soaked in n-octanol for 5 min and dried naturally to remove excess extraction solvent from its surface. Finally, the fiber tube was placed in a brown extraction bottle and stirred using a thermostatic magnetic stirrer at 40 ℃ and 1600 r/min for 2 h. After extraction, the three target analytes were separated on a Welch Ultimate XB-C18 column (250 mm×4.6 mm, 5 µm) under isocratic elution conditions using acetic acid aqueous solution and methanol (45∶55, v/v) as the eluent. The results indicated that the three PGRs showed good linearity in the range of 0.5-100.0 µg/L (coefficients of determination (r2)=0.9999), with limits of detection (LODs) of 0.02-0.15 µg/L. The method recoveries were 88.5-102.2%, with relative standard deviations (RSDs) of less than 3.7% (n=3). The extraction efficiencies and enrichment factors of the three PGRs in 15 batches of fresh D. officinale and A. roxburghii products were found to be 42.0%-86.8% and 140-289. Full-scan mass spectrometry was used to further identify positive samples to avoid false-positive results and enhance the reliability of the experimental method. In summary, the proposed method is sensitive, accurate, reliable, environment friendly, and capable of high enrichment. It could be used to determine the residues of three acidic PGRs in D. officinale and A. roxburghii. Moreover, it can provide technical support for the residue detection of PGRs in other Chinese medicinal materials.

Changes in insulin resistance, glucose effectiveness, and first and second phases of insulin secretion in women aged 45-60 years old in Taiwan.

BACKGROUND: In women after menopause, the incidence of diabetes mellitus increases. Increased insulin resistance (IR), decreased glucose effectiveness (GE), and the first and second phases of insulin secretion (FPIS and SPIS), are the four most important factors that trigger glucose intolerance and diabetes (diabetogenic factor [DF]). In the cross-sectional study, we enrolled nondiabetic women between the ages of 45 and 60 years to observe the changes in DFs during the perimenopausal period and to elucidate the underlying mechanisms of diabetes in menopausal women. METHODS: We randomly enrolled 4194 women who underwent health checkups. Using demographic and biochemical data, IR, FPIS, SPIS, and GE were calculated using previously published equations. The relationship between the DFs and age was evaluated using a simple correlation. RESULTS: Body mass index, blood pressure, fasting plasma glucose, low-density lipoprotein cholesterol, triglyceride, and SPIS were higher, and GE was lower in older women (≥52 years old). A significant decrease in GE and increased SPIS were observed with age. However, no changes were observed in IR or FPIS. CONCLUSION: The IR and FPIS did not change during perimenopause. Increased SPIS may compensate for the decrease in GE, which is probably one of the reasons for the higher incidence of diabetes in menopausal women.

Aging Affects Insulin Resistance, Insulin Secretion, and Glucose Effectiveness in Subjects with Normal Blood Glucose and Body Weight.

AIM: Several studies have demonstrated that factors including diabetes, including insulin resistance (IR), glucose effectiveness (GE), and the first and second phase of insulin secretion (FPIS, SPIS) could easily be calculated using basic characteristics and biochemistry profiles. Aging is accompanied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging in the different phases of insulin secretion (ISEC), i.e., the first and second phase of insulin secretion (FPIS, SPIS), and glucose effectiveness (GE). METHODS: In total, 169 individuals (43 men and 126 women) recruited from the data bank of the Meei-Jaw (MJ) Health Screening Center and Cardinal Tien Hospital Data Access Center between 1999 and 2008, with a similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. The IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS, and GE). The equations that are used to calculate the DF in the present study were built and published by our group. RESULTS: The age of the participants ranged from 18 to 78 years. Men had higher FPIS but lower HDL-C levels than women (2.067 ± 0.159, 1.950 ± 0.186 µU/min and 1.130 ± 0.306, 1.348 ± 0.357 mmol/dl, accordingly). The results of the Pearson correlation revealed that age was negatively related to the IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). At the same time, the FPIS was also only found to be negatively correlated with age in females (r = -0.238, p = 0.003), but there was no difference in the SPIS and age among both genders. CONCLUSIONS: We have found that in Chinese subjects with a normal FPG level (90 mg/dL) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to the IR and GE among both genders. Only the FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest, are GE, FPIS, and IR. These results should be interpreted with caution because of the small sample size.

In situ enrichment of sulphate-reducing microbial communities with different carbon sources stimulating the acid mine drainage sediments.

The applications of sulphate-reducing microorganisms (SRMs) in acid mine drainage (AMD) treatment systems have received extensive attention due to their ability to reduce sulphate and stabilize metal(loid)s. Despite great phylogenetic diversity of SRMs, only a few have been used in AMD treatment bioreactors. In situ enrichment could be an efficient approach to select new effective SRMs for AMD treatment. Here, we performed in situ enrichment of SRMs in highly stratified AMD sediment cores using different kinds of carbon source mixture. The dsrAB (dissimilatory sulfite reductase) genes affiliated with nine phyla (two archaeal and seven bacterial phyla) and 26 genera were enriched. Remarkably, those genes affiliated with Aciduliprofundum and Vulcanisaeta were enriched in situ in AMD-related environments for the first time, and their relative abundances were negatively correlated with pH. Furthermore, 107 dsrAB-containing metagenome-assembled genomes (MAGs) were recovered from metagenomic datasets, with 14 phyla (two archaeal and 12 bacterial phyla) and 15 genera. The relative abundances of MAGs were positively correlated with total carbon and sulphate contents. Our findings expanded the diversity of SRMs that can be enriched in AMD sediment, and revealed the physiochemical properties that might affect the growth of SRMs, which provided guidance for AMD treatment bioreators.

Temporal and double inverted internal limiting membrane flap for bilateral choroidal ruptures complicated by bilateral macular holes.

Choroidal ruptures occur in 5% to 10% closed-globe injuries with wide variation in visual prognosis, which depending on the visual acuity at presentation, the location of the rupture, and other associated ocular injuries. We reported a case of bilateral traumatic choroidal rupture with a large macular hole. We performed surgery in the right eye of microincisional vitrectomy, temporally inverted internal limiting membrane (ILM) flap, and C3F8 tamponade; then microincisional vitrectomy, fibrotic scar removal, double inverted ILM flap, and C3F8 tamponade in the left eye. After surgery, she achieved both good anatomical and visual acuity improvement in the right eye, but limited visual acuity improvement in the left eye due to subfoveal choroidal scar formation.

Using Machine Learning to Predict Abnormal Carotid Intima-Media Thickness in Type 2 Diabetes.

Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients.

Gasdermin A Is Required for Epidermal Cornification during Skin Barrier Regeneration and in an Atopic Dermatitis-Like Model.

Atopic dermatitis is featured with impaired skin barrier. The stratum corneum and the intercellular tight junctions constitute the permeability barrier, which is essential to protect water loss in the host and prevent pathogen entry. The epidermal barrier is constantly renewed by differentiating keratinocytes through cornification, during which autophagy contributes to elimination of organelles and nucleus. The human GSDMA and its mouse homologs Gsdma1-3 are expressed in the suprabasal epidermis. Although a pyroptotic role of GSDMA/Gsdma1 in host defense against Streptococcus pyogenes has been reported, the physiological function of Gsdma1/a2/a3 in epidermal homeostasis remains elusive. Here, through repeated epidermal barrier disruption, we found that tight junction formation and stratum corneum maturation were defective in the Gsdma1/a3-deficient epidermis. Using comparative gene profiling analysis, mitochondrial respiration measurement, and in vivo tracing of mitophagy, our data indicate that Gsdma1/a3 activation leads to mitochondrial dysfunction and subsequently facilitates mitochondrial turnover and epidermal cornification. In calcipotriol (MC903)-induced atopic dermatitis-like animal model, we showed that Gsdma1/a3-deficiency selectively enhanced the T helper type 2 response. Remarkably, the GSDMA expression is reduced in the epidermis of patients with atopic dermatitis compared with that of normal individuals. Gsdma1/a3-deficiency might be involved in atopic dermatitis pathogenesis, likely through GSDMA-mediated epidermal differentiation and cornification.

Two new halogenated metabolites from the red alga Laurencia sp.

Two new halogenated metabolites, laurenhalogens A (1) and B (2), along with four known ones (3-6), were isolated from the red alga Laurencia sp. The structures of 1 and 2 were determined by the means of UV, IR, MS, NMR and X-ray diffraction analysis. In addition, the antibacterial activities of 1-6 were also evaluated.

Corneal Complications after COVID-19 Vaccination: A Systemic Review.

Multiple vaccines are now being used across the world, and several studies have described cases of corneal graft rejection following the administration of the COVID-19 vaccine. The purpose of this article is to review the corneal adverse event that occurred following COVID-19 vaccine administration. The literature search was conducted in March 2022 using MEDLINE, PubMed, and the Cochrane Database of Systematic Reviews. A total of 27 articles, including 37 cases, have documented corneal adverse events that occurred following COVID-19 vaccination. The mean age was 60 ± 14.9 years (range, 27-83 years). The most common events were acute corneal graft rejection (n = 21, 56.8%), followed by herpes zoster ophthalmicus (n = 11, 29.7%) and herpes simplex keratitis (n = 2, 5.4%). The mean time from vaccination to the event was 10 ± 8.5 days (range, 1-42 days) after the first or second dose of vaccine. All patients with corneal graft rejection, immune-mediated keratolysis, and peripheral ulcerative keratitis (PUK) (n = 24, 64.9%) were managed topically with or without oral corticosteroids. Patients with herpes zoster ophthalmicus and herpes simplex keratitis were managed with oral antiviral agents. Two patients received penetrating keratoplasty due to keratolysis after invalid topical treatment. Disease resolution was noted in 29 patients (78.3%), whereas 3 (8.1%) had persistent corneal edema after graft rejection, 1 (2.7%) had corneal infiltration after HZO, and 4 (10.8%) were not mentioned in the articles. Corneal adverse events could occur after COVID-19 vaccination. After timely treatment with steroids or antiviral agents, most of the events were mild and had a good visual outcome. Administrating or increasing steroids before vaccination may be useful for the prevention of corneal graft rejection. However, the prophylactic use of antiviral treatments in patients with a herpes viral infection history is not recommend.

Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.

The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.