The CREB1/WNK1 axis promotes the tumorigenesis of ovarian cancer via regulating HIF-1.

The aim of this study was to explore the functions and molecular mechanisms of the WNK lysine deficient protein kinase 1 (WNK1) in the development of ovarian cancer. Firstly, loss- and gain-of-function assays were carried out and subsequently cell proliferation, apoptosis, invasion and migration were detected. Furthermore, WNK1 action on glucose uptake, lactate production and adenosine triphosphate (ATP) level were assessed. The roles of WNK1 on cisplatin resistance were explored using CCK-8, colony formation, and flow cytometry in vitro. Immunohistochemistry, Western blot and qRT-PCR were conducted to determine the protein and mRNA expression. Additionally, tumor growth in vivo was also monitored. We found that the overexpression of WNK1 predicted a bad prognosis of ovarian cancer patients. WNK1 enhanced the malignant behavior and facilitated glycolysis of ovarian cancer cells. Moreover, WNK1 increased cisplatin resistance in ovarian cancer cells. Mechanistically, we found that WNK1 expression was promoted by CREB1 at the transcriptional level. And CREB1 could facilitate ovarian cancer cells malignant behavior through target upregulating WNK1. Besides, we also showed that WNK1 facilitated the malignant behavior by accelerating HIF-1 expression. In xenograft tumor tissues, the downregulation of WNK1 significantly reduced HIF-1α expression. These data demonstrated that the CREB1/WNK1 axis could promote the tumorigenesis of ovarian cancer via accelerating HIF-1 expression, suggesting that the CREB1/WNK1 axis could be a potential target during the therapy of ovarian cancer.

High-quality chromosome-level genomic insights into molecular adaptation to low-temperature stress in Madhuca longifolia in southern subtropical China.

BACKGROUND: Madhuca longifolia, the energy-producing and medicinal tropical tree originally from southern India, faces difficulties in adapting to the low temperatures of late autumn and early winter in subtropical southern China, impacting its usability. Therefore, understanding the molecular mechanisms controlling the ability of this species to adapt to environmental challenges is essential for optimising horticulture efforts. Accordingly, this study aimed to elucidate the molecular responses of M. longifolia to low-temperature stress through genomic and transcriptomic analyses to inform strategies for its effective cultivation and utilisation in colder climates. RESULTS: Herein, the high-quality reference genome and genomic assembly for M. longifolia are presented for the first time. Using Illumina sequencing, Hi-C technology, and PacBio HiFi sequencing, we assembled a chromosome-level genome approximately 737.92 Mb in size, investigated its genomic features, and conducted an evolutionary analysis of the genus Madhuca. Additionally, using transcriptome sequencing, we identified 17,941 differentially expressed genes related to low-temperature response. Through bioinformatics analysis of the WRKY gene family, 15 genes crucial for M. longifolia low-temperature resistance were identified. CONCLUSIONS: This research not only lays the groundwork for the successful ecological adaptation and cultivation of M. longifolia in China's southern subtropical regions but also offers valuable insights for the genetic enhancement of cold tolerance in tropical species, contributing to their sustainable horticulture and broader industrial, medicinal, and agricultural use.

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects.

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

Pivoting from in-person to phone survey assessment of alcohol and substance use: effects on representativeness in a United States prospective cohort of women living with and without HIV.

Background: Many clinical and population-based research studies pivoted from in-person assessments to phone-based surveys due to the COVID-19 pandemic. The impact of these transitions on survey response remains understudied, especially for people living with HIV. Given that there are gender-specific trends in alcohol and substance use, it is particularly important to capture these data for women.Objective: Identify factors associated with responding to an alcohol and substance use phone survey administered during the COVID-19 pandemic in the Women's Interagency HIV Study, a multicenter US prospective cohort of women living with and without HIV.Methods: We used multivariable logistic regression to assess for associations of pre-pandemic (April-September 2019) sociodemographic factors, HIV status, housing status, depressive symptoms, alcohol use, and substance use with response to an early-pandemic (August-September 2020) phone survey.Results: Of 1,847 women who attended an in-person visit in 2019, 78% responded to a phone survey during the pandemic. The odds of responding were lower for women of Hispanic ethnicity (aOR 0.47 95% CI 0.33-0.66, ref=Black/African American) and those who reported substance use (aOR 0.63 95% CI 0.41-0.98). By contrast, the odds were higher for White women (aOR 1.64 95% CI 1.02-2.70, ref=Black/African American) and those with stable housing (aOR 1.74 95% CI 1.24-2.43).Conclusions: Pivoting from an in-person to phone-administered alcohol and substance use survey may lead to underrepresentation of key subpopulations of women who are often neglected in substance use and HIV research. As remote survey methods become more common, investigators need to ensure that the study population is representative of the target population.

Comparison of blended learning and traditional lecture method on learning outcomes in the evidence-based medicine course: a comparative study.

BACKGROUND: Blended learning comprised with flipped classroom (FC) and "internet plus" is a new learning strategy that reverses the position of teacher and students in class, and provides abundant learning resources before and after class. This study aimed to assess the impact of blended learning on learning outcomes in evidence-based medicine course, and compare with traditional learning method. METHODS: The participants of the two groups were from two difference cohorts in Air force medical university in China. The two groups toke the same pre-test before class and then were given the teaching of same chapters of evidence-based medicine with two different learning strategy. In the blended learning group, the participants were required to create a debriefing slide about their learning outcomes and the answers of questions given in advance after study the learning material sent by teacher a week before class, and the teacher gave a detailed summary based on the common problems, and distributed multimedia resources for review. After the experiment was carried out, learning outcomes including mastering knowledge, learning satisfaction, and self-evaluation were compared. RESULTS: 37 and 39 participants were enrolled to blended learning and traditional learning groups, respectively, and no statistically significant difference were found in baseline information and pre-test grades. Statistically significant differences were found in learning outcomes including post-test score (t = 2.90, p = 0.005), changes of scores between pre-test and post-test (t = 2.49, p = 0.022), learning satisfaction (t = 12.41, p = 0.001), and self-evaluation of the two groups (t = 7.82, p = 0.001). Especially, the changes of scores between pre-test and post-test of blended learning and traditional learning groups were 4.05 (4.26), and 2.00 (2.85), respectively. CONCLUSIONS: This study showed that compared with traditional learning strategy, blended learning can effectively enhanced participants' acquisition of knowledge, learning satisfaction, and self-evaluation in evidence-based medicine. Using blended learning method including "internet plus" and flipped classroom is recommended in the teaching of evidence-based medicine course.

Establishment and Verification of a Novel Gene Signature Connecting Hypoxia and Lactylation for Predicting Prognosis and Immunotherapy of Pancreatic Ductal Adenocarcinoma Patients by Integrating Multi-Machine Learning and Single-Cell Analysis.

Pancreatic ductal adenocarcinoma (PDAC) has earned a notorious reputation as one of the most formidable and deadliest malignant tumors. Within the tumor microenvironment, cancer cells have acquired the capability to maintain incessant expansion and increased proliferation in response to hypoxia via metabolic reconfiguration, leading to elevated levels of lactate within the tumor surroundings. However, there have been limited studies specifically investigating the association between hypoxia and lactic acid metabolism-related lactylation in PDAC. In this study, multiple machine learning approaches, including LASSO regression analysis, XGBoost, and Random Forest, were employed to identify hub genes and construct a prognostic risk signature. The implementation of the CERES score and single-cell analysis was used to discern a prospective therapeutic target for the management of PDAC. CCK8 assay, colony formation assays, transwell, and wound-healing assays were used to explore both the proliferation and migration of PDAC cells affected by CENPA. In conclusion, we discovered two distinct subtypes characterized by their unique hypoxia and lactylation profiles and developed a risk score to evaluate prognosis, as well as response to immunotherapy and chemotherapy, in PDAC patients. Furthermore, we indicated that CENPA may serve as a promising therapeutic target for PDAC.

Characteristics and performance of algal-bacterial granular sludge in photo-sequencing batch reactors under various substrate loading rates.

The algae-bacterial granular sludge (ABGS) technology has garnered significant attention due to its remarkable attributes of low carbon emissions. To investigate the performance of the ABGS system under various substrate loading rates, the parallel photo-sequencing batch reactors (P1 and P2) were set up. The results indicated that chlorophyll-a content and extracellular polymeric substance content were measured at 10.7 ± 0.3 mg/L and 61.4 ± 0.7 mg/g SS in P1 under relatively low substrate loading rate (0.9 kg COD/m3/d and 0.09 kg N/m3/d). Moreover, kinetic study revealed that the maximal specific P uptake rate for P1 reached 0.21 mg P/g SS/h under light conditions, and it achieved 0.078 mg P/g SS/h under dark conditions, highlighting the significant role on phosphorus removal played by algae in the ABGS system. The microbial analysis and scanning electron microscopy confirmed that filamentous algae predominantly colonize the surface in P1, whereas spherical bacteria dominate the surface of granular sludge in P2. Additionally, a diverse array of microorganisms including bacteria, algae, and metazoa such as Rotifers and Nematodes were observed in both systems, providing evidence for the establishment of a symbiotic system. This study not only confirmed the ability of ABGS for efficient N and P removal under different substrate loading conditions but also highlighted its potential to enhance the ecological diversity of the reaction system.

Longitudinal Assessment of the Enhanced Liver Fibrosis Score in the Era of Contemporary HIV and Hepatitis C Virus Treatment.

BACKGROUND: The trajectory of liver fibrosis is not well understood in the contemporary era of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) therapy. METHODS: We assessed the Enhanced Liver Fibrosis (ELF) score, aspartate transaminase-to-platelet ratio index (APRI) and Fibrosis-4 (FIB-4) in 116 women with HIV/HCV coinfection over a 4-year period. Random-effects linear regression models examined the rate of fibrosis change 1-2 years before starting HCV treatment, within 1 year before starting (peri-HCV treatment), within 1 year after and 1-2 years post-HCV treatment in unadjusted and adjusted models including age, race, and changes from pretreatment of factors that might affect fibrosis (eg, alcohol, integrase strand inhibitor [INSTI] use, waist circumference, CD4 count). RESULTS: INSTI use nearly doubled from pre- to peri-HCV treatment. In unadjusted analysis, there was a 3.3% rate of rise in ELF pre-HCV treatment, 2.2% and 3.6% rate of decline during the peri- and 1-year post-HCV treatment period, respectively, followed by a 0.3% rise. Similar findings were observed for APRI and FIB-4. There was little effect on the estimated fibrosis trajectories after adjustment. CONCLUSIONS: The apparent lack of decline in biomarkers of liver fibrosis beyond 1 year after HCV cure suggests that continued monitoring of liver fibrosis and interventions to mitigate progression in people with HIV after HCV cure remains essential.

Body Composition Changes Over the Menopausal Transition in Women With and Without Human Immunodeficiency Virus.

BACKGROUND: Women are at risk for weight gain during the transition to menopause, but few have examined the contribution of menopause to weight gain in women with human immunodeficiency virus (WWH). METHODS: From 2000 to 2013, participants (621 WWH; 218 without HIV [WWOH]) from the Women's Interagency HIV Study were categorized by menopausal phase using serial measures of anti-Müllerian hormone (AMH). Multivariable linear mixed models examined the association of menopausal phase with body mass index (BMI) and waist circumference (WC) trajectory, stratified by HIV status. RESULTS: In models controlled for chronologic age, the estimated effects (95% confidence interval) of menopausal phase on annual rate of BMI change across early perimenopause, late perimenopause, and menopause, respectively, compared to premenopause were -0.55% (-.80 to -.30), -0.29% (-.61 to .03), and -0.67% (-1.12 to -.20) in WWH, whereas estimated effects were 0.43% (-.01 to .87) and 0.15% (-.42 to .71) across early and late perimenopause, respectively, and -0.40% (-1.24 to .45) across menopause in WWOH. The estimated effects on rate of WC change were negative across early perimenopause (-0.21% [-.44 to .03]) and menopause (-0.12% [-.5 to .26]) and positive across late perimenopause (0.18% [-.10 to .45]) in WWH, and positive across all 3 menopausal phases in WWOH, but these effects were not statistically significant. CONCLUSIONS: In WWH, the menopausal transition was associated with BMI and WC trajectories that were mostly in a negative direction and opposite from WWOH after adjusting for age, suggesting that HIV blunts weight gain during the menopausal transition.

Peripheral Polyfunctional PD1+ CD8+ T cells demonstrated strong immune protection in non-small cell lung cancer.

Higher frequencies of polyfunctional PD1+ CD8+ T cells exhibited a stronger capacity to kill tumor cells in vitro and in vivo experiments. These results suggested that peripheral polyfunctional PD1+ CD8+ T cells demonstrated strong immune protection. This study also provided a potential combined treatment strategy with anti-PD1 and CAR-T therapy.

Position-Induced Controllable Growth of Vertically Oriented Graphene Using Plasma-Enhanced Chemical Vapor Deposition.

Because the morphology of vertically oriented graphene (VG) synthesized by the plasma-enhanced chemical vapor deposition process determines the application performance of VG, morphology control is always an important part of the research. A concise correspondence between plasma and the morphology of VG is the key to investigating the morphology control of VG, which is still under research. In this study, a simple but effective parameter, position, is used to grow VG, by which the continuous morphology evolution of VG is realized. As a result, the morphology of VGs varies from a porous structure to a "wall-like" structure, thus leading to a continuous change in its hydrophobicity and thermal emissivity. An ultrahigh emissivity of 0.999 with superhydrophobicity is obtained among these VGs, showing great potential in the area of the black body and infrared thermometer. Finally, the states of active particles in plasma depending on the positions are diagnosed to investigate their relations with the morphology of VGs.

Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.

Hand, foot and mouth disease (HFMD) is a common infection in the world, and its epidemics result in heavy disease burdens. Over the past decade, HFMD has been widespread among children in China, with Shanxi Province being a severely affected northern province. Located in the temperate monsoon climate, Shanxi has a GDP of over 2.5 trillion yuan. It is important to have a comprehensive understanding of the basic features of HFMD in those areas that have similar meteorological and economic backgrounds to northern China. We aimed to investigate epidemiological characteristics, identify spatial clusters and predict monthly incidence of HFMD. All reported HFMD cases were obtained from the Shanxi Center for Disease Control and Prevention. Overall HFMD incidence showed a significant downward trend from 2017 to 2020, increasing again in 2021. Children aged < 5 years were primarily affected, with a high incidence of HFMD in male patients (relative risk: 1.316). The distribution showed a seasonal trend, with major peaks in June and July and secondary peaks in October and November with the exception of 2020. Other enteroviruses were the predominant causative agents of HFMD in most years. Areas with large numbers of HFMD cases were primarily in central Shanxi, and spatial clusters in 2017 and 2018 showed a positive global spatial correlation. Local spatial autocorrelation analysis showed that hot spots and secondary hot spots were concentrated in Jinzhong and Yangquan in 2018. Based on monthly incidence from September 2021 to August 2022, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models were 386.58 vs. 838.25, 2.25 vs. 3.08, and 461.96 vs. 963.13, respectively, indicating that the predictive accuracy of LSTM was better than that of SARIMA. The LSTM model may be useful in predicting monthly incidences of HFMD, which may provide early warnings of HFMD epidemics.

Virtual screening of novel mTOR inhibitors for the potential treatment of human colorectal cancer.

The abnormal activation of the mTOR pathway is closely related to the occurrence and progression of cancer, especially colorectal cancer. In this study, a rational virtual screening strategy has been established and MT-5, a novel mTOR inhibitor with a quinoline scaffold, was obtained from the ChemDiv database. MT-5 showed potent kinase inhibitory activity (IC50: 8.90 µM) and antiproliferative effects against various cancer cell lines, especially HCT-116 cells (IC50: 4.61 µM), and this was 2.2-fold more potent than that of the cisplatin control (IC50: 9.99 µM). Western blot, cell migration, cycle arrest, and apoptosis assays were performed with HCT-116 cells to investigate the potential anticancer mechanism of MT-5. Metabolic stability results in vitro indicated that MT-5 exhibited good stability profiles in artificial gastrointestinal fluids, rat plasma, and liver microsomes. In addition, the key contribution of the residues around the binding pocket of MT-5 in binding to the mTOR protein was also investigated from a computational perspective.

The mediating effects of depression, anxiety, and rapid eye movement sleep behavior disorder on the association between dopaminergic replacement therapy and impulse control disorders in Parkinson's disease.

OBJECTIVES: This study aims to longitudinally explore whether and how rapid eye movement sleep behavior disorder (RBD), depression, and anxiety mediate the association between dopaminergic replacement therapy (DRT) and impulse control disorders (ICDs) in patients with Parkinson's disease (PD). METHODS: Subjects were selected from the Parkinson's Progression Markers Initiative. After excluding missing data, 268, 223, 218, 238, and 219 patients with PD diagnosed at 12, 24, 36, 48, and 60 months prior, respectively, were included. We used the Questionnaire for Impulsive-Compulsive Disorders, RBD Screening Questionnaire, Geriatric Depression Scale, and State-Trait-Anxiety Inventory to assess ICBs, RBD, depression, and anxiety, respectively. We constructed three causal mediation analysis models to infer potential contingent pathways from DRT to ICD mediated by depression, anxiety, and RBD separately. RESULTS: DRT was associated with an increased risk of PD incidence. Aggravation of ICDs was partly explained by improvements in depression (the average causal mediation effect accounted for 8.0% of the total effect) and RBD (the average causal mediation effect of RBD accounted for 16.4% of the total effect). This suggested that anxiety (the average causal mediation effect accounted for 12.7% of the total effect) plays a mediating role. CONCLUSIONS: Focusing on changes in RBD, depression, and anxiety associated with hyperdopaminergic status should be an essential part of strategies to prevent ICDs in patients with Parkinson's disease.

Equal-intensity beam splitter realization by wire grid polarizers for passive laser speckle reduction.

A method to realize an equal-intensity beam splitter (EIBS) using wire grid polarizers (WGPs) is proposed. The EIBS consists of WGPs with predetermined orientations and high-reflectivity mirrors. We demonstrated the generation of three laser sub-beams (LSBs) with equivalent intensities using EIBS. The three LSBs were incoherent by introducing optical path differences larger than the laser coherence length. The LSBs were used to reduce speckle passively, where the objective speckle contrast was reduced from 0.82 to 0.5 when all three LSBs were used. The feasibility of EIBS in speckle reduction was studied using a simplified laser projection system. The structure of the EIBS implemented by WGPs is simpler than EIBSs obtained by other methods.

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China.

BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Comparative Efficacy of Obstructive Sleep Apnea Patients Undergoing Multilevel Surgery Followed by Upper Airway Stimulation Versus Isolated Upper Airway Stimulation.

PURPOSE: Upper Airway Stimulation (UAS) of the hypoglossal nerve is a rapidly growing management option for patients with obstructive sleep apnea (OSA). Our study compares the treatment efficacy of UAS between those who were initially ineligible for UAS but subsequently met eligibility after multilevel surgery versus those who underwent isolated UAS for the treatment of moderate to severe OSA. METHODS: The investigators implemented a retrospective single-center cohort study of patients aged 18+ years who presented for surgical evaluation of OSA from 2016-2019 and underwent UAS implantation. The predictor variable was eligibility status for UAS. Initially ineligible subjects were defined as having an apnea-hypopnea (AHI) > 65 events/hr, body mass index (BMI) > 32 kg/m2, or complete concentric collapse (CCC) on drug-induced sleep endoscopy. Eligible subjects were defined as having an AHI between 15 and 65, with no CCC on drug-induced sleep endoscopy. The primary outcome was change in AHI which was measured preoperatively and 6 months post UAS implantation. Secondary outcomes were change in Epworth sleepiness scale and Fatigue severity scale. Covariates were age, sex, and BMI. Data analysis involved descriptive statistics and multivariable statistical models; P < .05 was considered significant. RESULTS: Thirty six patients underwent UAS implantation from 2016-2019. Eighteen patients who were initially ineligible for UAS underwent multilevel surgery, including uvulopalatopharyngoplasty, distraction osteogenesis maxillary expansion, or maxillomandibular advancement. Mean age was 62.4 ± 9 years and BMI of 29.1 ± 4 kg/m2 with 5 female patients. The cohort of 17 patients who met criteria for UAS from the start had a mean age of 62.9 ± 14 years and mean BMI of 26.7 ± 4 kg/m2 with 2 female patients. Mean AHI reduction for the multilevel group was 37.6 ± 21.2 events per hour (P < .001). Mean AHI reduction for the UAS-only group was 31.5 ± 13 events per hour (P < .001). When adjusted for age, BMI, and sex, the multilevel group had a more significant reduction (18 AHI events) compared to the isolated group (P < .001). CONCLUSIONS: For patients who are ineligible for UAS due to severity of OSA or CCC of the velum, multilevel surgery including maxillomandibular advancement followed by UAS confers effective post-treatment results, which was superior to the UAS-only group.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.

BACKGROUND: Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. METHODS: We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. RESULTS: Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset. CONCLUSIONS: The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Effect of minute amounts of arsenic on the sulfamethoxazole removal and microbial community structure via the SBR system.

In this study, the effect of arsenic on the sulfamethoxazole (SMX) removal efficiency and microbial community structure was investigated over 60 days using the SBR process. The results showed that the presence of arsenic had no significant impact on the system performance, the removal efficiencies of two reactors, R1 (the control test) and R2 (with the addition of arsenic), were 13.36 ± 5.71 and 14.20 ± 5.27%, which were attributed to the adsorption of SMX by fulvic acid-like substances and tryptophan-like proteins of extracellular polymeric substances. Compared to the seed sludge, the species number indicated that R2 possessed the richer diversity, while R1 possessed the lower diversity on day 60, which might be relative to the transferring of antibiotic resistance genes (ARGs) in sludge bacterial communities; the minute amounts of arsenic could make the relative levels of Sul1 and Sul2 genes which encode ARGs of sulfonamides in R2 (2.07 and 2.47%) be higher than that in R1 (1.65 and 1.27%), which made the bacterial community of the R2 system more adaptable to SMX stress. Therefore, the minute amounts of arsenic weakened the effect of SMX on the system and enhanced the stability of the microbial community structure.

Human Immunodeficiency Virus Is Associated With Elevated FibroScan-Aspartate Aminotransferase (FAST) Score.

BACKGROUND: Whether human immunodeficiency virus (HIV) infection is associated with the development of nonalcoholic steatohepatitis (NASH) remains unclear. The FibroScan-aspartate aminotransferase (FAST) score was developed to identify patients who have histologic NASH with high nonalcoholic fatty liver disease activity score (NAS ≥4) and significant liver fibrosis (≥F2), which has been associated with higher risk of end-stage liver disease. We examined whether HIV infection is associated with elevated FAST score in a large United States (US) cohort. METHODS: Vibration-controlled transient elastography was performed in 1309 women without history of chronic viral hepatitis enrolled from 10 US sites: 928 women with HIV (WWH) and 381 women without HIV (WWOH). We used multivariable logistic regression to evaluate associations of HIV, demographic, lifestyle, and metabolic factors with an elevated (>0.35) FAST score. RESULTS: Median age of WWH and WWOH was 51 years and 48 years, respectively. Most (90%) WWH were on antiretroviral therapy and 72% had undetectable HIV RNA. Prevalence of elevated FAST score was higher among WWH compared to WWOH (6.3% vs 1.8%, respectively; P = .001). On multivariable analysis, HIV infection was associated with 3.7-fold higher odds of elevated FAST score (P = .002), and greater waist circumference (per 10 cm) was associated with 1.7-fold higher odds (P < .001). In analysis limited to WWH, undetectable HIV RNA and current protease inhibitor use were independently associated with lower odds of elevated FAST score. CONCLUSIONS: Our findings suggest that HIV is an independent risk factor for NASH with significant activity and fibrosis. Studies validating FAST score in persons with HIV are warranted.