LSTM-Based Prediction Model for Tuberculosis Among HIV-Infected Patients Using Structured Electronic Medical Records: A Retrospective Machine Learning Study.

Background: Both HIV and TB are chronic infectious diseases requiring long-term treatment and follow-up, resulting in extensive electronic medical records. With the exponential growth of health and medical big data, effectively extracting and analyzing these data has become the research hotspot. As a fundamental aspect of artificial intelligence, machine learning has been extensively applied in medical research, encompassing diagnosis, treatment, patient monitoring, drug development, and epidemiological investigations. This significantly enhances medical information systems and facilitates the interoperability of medical data. Methods: In our study, we analyzed longitudinal data from the electronic health records of 4540 patients, gathered from the National Clinical Research Center for Infectious Diseases in Shenzhen, China, spanning from 2017 to 2021. Initially, we employed the fine-tuned ChatGLM to structure the electronic medical records. Subsequently, we utilized a multi-layer perceptron to classify each patient and determined the presence of tuberculosis in HIV patients. Using machine learning-based natural language processing, we structured these records to build a specialized database for HIV and TB co-infection. We studied the epidemiological characteristics, focusing on incidence patterns, patient characteristics, and influencing factors, to uncover the transmission characteristics of these diseases in Shenzhen. Additionally, we used Long Short-Term Memory to create a predictive model for TB co-infection among HIV patients, based on their medical records. This model predicted the risk of TB co-infection, providing scientific evidence for clinical decision-making and enabling early detection and precise intervention. Results: Based on the refined ChatGLM model tailored for structured electronic health records, the accuracy of symptom extraction consistently surpassed 0.95 precision. Key symptoms such as diarrhea and normal showed precision rates exceeding 0.90. High scores were also achieved in recall and F1 scores. Among 4540 HIV patients, 758 were diagnosed with concurrent tuberculosis, indicating a 16.7% co-infection rate, while syphilis co-infection affected 25.1%, underscoring the prevalence of concurrent infections among HIV patients. Utilizing electronic health records, a Multilayer Perceptron classifier was developed as a benchmark against Long Short-Term Memory to predict high-risk groups for HIV and tuberculosis co-infections. The Multilayer Perceptron classifier demonstrated predictive ability with AUROC values ranging from 0.616 to 0.682 on the test set, suggesting opportunities for further optimization and generalization despite its accuracy in identifying HIV-TB co-infections. In tuberculosis intelligent diagnosis based on laboratory results, the Long Short-Term Memory showed consistent performance across 5-fold cross-validation, with AUROC values ranging from 0.827 to 0.850, indicating reliability and consistency in tuberculosis prediction. Furthermore, by optimizing classification thresholds, the model achieved an overall accuracy of 81.18% in distinguishing HIV co-infected tuberculosis from simple HIV infection. Conclusion: Combining the Multilayer Perceptron classifier with Long Short-Term Memory represented an advanced approach for effectively extracting electronic health records and utilizing it for disease prediction. This underscored the superior performance of deep learning techniques in managing both structured and unstructured medical data. Models leveraging laboratory time-series data demonstrated notably better performance compared to those relying solely on electronic health records for predicting tuberculosis incidence. This emphasized the benefits of deep learning in handling intricate medical data and provided valuable insights for healthcare providers exploring the use of deep learning in disease prediction and management.

Chlamydia psittaci detected at a live poultry wholesale market in central China.

BACKGROUND: We investigated the presence of Chlamydia psittaci in poultry and the environment in live poultry wholesale markets in Changsha during 2021-2022 and conducted a phylogenetic analysis to understand its distribution in this market. METHODS: In total, 483 samples were analyzed using real-time polymerase chain reaction and 17 C. psittaci-positive samples using high-throughput sequencing, BLAST similarity, and phylogenetic analysis. RESULTS: Twenty-two out of 483 poultry and environmental samples were positive for C. psittaci (overall positivity rate: 4.55%) with no difference in positivity rates over 12 months. Chlamydia psittaci was detected at 11 sampling points (overall positivity rate: 27.5%), including chicken, duck, and pigeon/chicken/duck/goose shops, with pigeon shops having the highest positivity rate (46.67%). The highest positivity rates were found in sewage (12.5%), poultry fecal (7.43%), cage swab (6.59%), avian pharyngeal/cloacal swab (3.33%), and air (2.29%) samples. The ompA sequences were identified in two strains of C. psittaci, which were determined to bear genotype B using phylogenetic analysis. Thus, during monitoring, C. psittaci genotype B was detected in the poultry and environmental samples from the poultry wholesale market in Changsha. CONCLUSIONS: To address the potential zoonotic threat, C. psittaci monitoring programs in live poultry markets should be enhanced.

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.

OBJECTIVE: To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). METHODS: This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. RESULTS: The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups. CONCLUSIONS: The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

Contrast-Enhanced Ultrasound-Guided Endoscopic Retrograde Appendicitis Treatment-A Pilot Study on the Diagnostic and Therapeutic Efficacy in Uncomplicated Appendicitis.

OBJECTIVE: To determine the diagnostic and therapeutic value of contrast-enhanced ultrasound-guided endoscopic retrograde appendicitis treatment (ERAT) in patients with uncomplicated appendicitis. METHODS: A retrospective analysis was performed on clinical and ultrasound data collected from 105 pediatric patients with uncomplicated appendicitis between January 2020 and December 2023. The ultrasound findings before and after treatment, as well as postoperative follow-up and recurrence rates, were summarized and analyzed. RESULTS: Successful intubation was achieved in 96 patients (91.4%). The conventional ultrasound appendix visualization rate was 39.6% (38/105), while the appendix visualization rate after contrast-enhanced ultrasound-guidance was 75% (72/105). Contrast-enhanced ultrasound revealed various appendiceal morphologic changes in 89 patients, such as twisting, tortuosity, stiffness, rough inner wall, dilated diameter, and narrowing of the lumen. Additionally, local filling defects, which indicated the presence of fecal stones or debris deposition, were noted in 68 patients. No leakage of the contrast agent occurred. Post-treatment evaluation showed improvement in appendiceal diameter, lumen, and filling defects (P < .01). The follow-up rate was 82 of 89 patients (92.1%), all of whom recovered well without a recurrence. The recurrence rate was 7.9% (7/89). Among the patients with recurrences, five patients resolved after medical treatment and two patients recovered after surgical treatment. CONCLUSION: Contrast-enhanced ultrasound-guided ERAT for uncomplicated appendicitis is safe and effective. Specifically, the appendix is increased, which facilitates an evaluation of therapeutic effectiveness. ERAT serves as a valuable supplementary modality to determine the need for surgical treatment of acute appendicitis, which is of significant clinical value.

c-Cbl induced podocin ubiquitination contributes to the podocytes injury in diabetic nephropathy.

The ubiquitination function in diabetic nephropathy (DN) has attracted much attention, but there is a lack of information on its ubiquitylome profile. To examine the differences in protein content and ubiquitination in the kidney between db/db mice and db/m mice, we deployed liquid chromatography-mass spectrometry (LC-MS/MS) to conduct analysis. We determined 145 sites in 86 upregulated modified proteins and 66 sites in 49 downregulated modified proteins at the ubiquitinated level. Moreover, 347 sites among the 319 modified proteins were present only in the db/db mouse kidneys, while 213 sites among the 199 modified proteins were present only in the db/m mouse kidneys. The subcellular localization study indicated that the cytoplasm had the highest proportion of ubiquitinated proteins (31.87%), followed by the nucleus (30.24%) and the plasma membrane (20.33%). The enrichment analysis revealed that the ubiquitinated proteins are mostly linked to tight junctions, oxidative phosphorylation, and thermogenesis. Podocin, as a typical protein of slit diaphragm, whose loss is a crucial cause of proteinuria in DN. Consistent with the results of ubiquitination omics, the K261R mutant of podocin induced the weakest ubiquitination compared with the K301R and K370R mutants. As an E3 ligase, c-Cbl binds to podocin, and the regulation of c-Cbl can impact the ubiquitination of podocin. In conclusion, in DN, podocin ubiquitination contributes to podocyte injury, and K261R is the most significant site. c-Cbl participates in podocin ubiquitination and may be a direct target for preserving the integrity of the slit diaphragm structure, hence reducing proteinuria in DN.

Risk Factors of Tuberculosis Destroyed Lung in Patients with Pulmonary Tuberculosis and Structural Lung Diseases: A Retrospective Observational Study.

Background: Tuberculosis destroyed lung constitutes a significant worldwide public health challenge, little is known about its associated risk factors and prognosis. Our study aimed to identify the risk factors of tuberculosis destroyed lung among pulmonary tuberculosis and structural lung diseases. Methods: Between January 2019 and December 2021, a case-control study was conducted at the Third People's Hospital of Shenzhen in China. We collected the clinical data among patients with pulmonary tuberculosis and structural lung diseases. Cases were defined as patients with tuberculosis destroyed lung. Controls were not diagnosed with the tuberculosis destroyed lung. A binary logistic regression was performed. Results: In our study, a total of 341 patients met the inclusion criteria, including 182 cases and 159 controls. We found that age ranges of 46-60 years (aOR: 4.879; 95% CI: 2.338-10.180), >60 years (aOR: 3.384; 95% CI: 1.481-7.735); history of TB treatment (aOR: 2.729; 95% CI: 1.606-4.638); malnutrition (aOR: 5.126; 95% CI: 1.359-19.335); respiratory failure (aOR: 5.080; 95% CI: 1.491-17.306); and bronchiarctia (aOR: 3.499; 95% CI: 1.330-9.209) were the independent risk factors for tuberculosis destroyed lung. Conversely, having a normal (aOR: 0.207; 95% CI: 0.116-0.371) or overweight BMI (aOR: 0.259; 95% CI: 0.090-0.747) emerged as a protective factor against tuberculosis destroyed lung. Conclusion: This study indicated that tuberculosis destroyed lung is a common condition among patients with pulmonary tuberculosis and structural lung diseases. The independent risk factors for tuberculosis destroyed lung were identified as being within the age groups of 46-60 and over 60 years, having a previous history of TB treatment, malnutrition, respiratory failure, and bronchiarctia. It is essential to closely monitor patients possessing these risk factors to prevent the progression towards tuberculosis destroyed lung.

ACSF2 and lysine lactylation contribute to renal tubule injury in diabetes.

AIMS: Lactate accumulation is reported to be a biomarker for diabetic nephropathy progression. Lactate drives lysine lactylation, a newly discovered post-translational modification that is involved in the pathogenesis of cancers and metabolic and inflammatory disease. Here, we aimed to determine whether lysine lactylation is involved in the pathogenesis of diabetic nephropathy. METHODS: Renal biopsy samples from individuals with diabetic nephropathy (n=22) and control samples from individuals without diabetes and kidney disease (n=9) were obtained from the First Affiliated Hospital of Zhengzhou University for immunohistochemical staining. In addition, we carried out global lactylome profiling of kidney tissues from db/m and db/db mice using LC-MS/MS. Furthermore, we assessed the role of lysine lactylation and acyl-CoA synthetase family member 2 (ACSF2) in mitochondrial function in human proximal tubular epithelial cells (HK-2). RESULTS: The expression level of lysine lactylation was significantly increased in the kidneys of individuals with diabetes as well as in kidneys from db/db mice. Integrative lactylome analysis of the kidneys of db/db and db/m mice identified 165 upregulated proteins and 17 downregulated proteins, with an increase in 356 lysine lactylation sites and a decrease in 22 lysine lactylation sites decreased. Subcellular localisation analysis revealed that most lactylated proteins were found in the mitochondria (115 proteins, 269 sites). We further found that lactylation of the K182 site in ACSF2 contributes to mitochondrial dysfunction. Finally, the expression of ACSF2 was notably increased in the kidneys of db/db mice and individuals with diabetic nephropathy. CONCLUSIONS: Our study strongly suggests that lysine lactylation and ACSF2 are mediators of mitochondrial dysfunction and may contribute to the progression of diabetic nephropathy. DATA AVAILABILITY: The LC-MS/MS proteomics data have been deposited in the ProteomeXchange Consortium database ( https://proteomecentral.proteomexchange.org ) via the iProX partner repository with the dataset identifier PXD050070.

A novel approach for ASD recognition based on graph attention networks.

Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects' fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the BOLD signal in each ROI, extract wavelet features, and utilize them along with the mean and variance of the BOLD signal as node features, and the optimized FC matrix as the adjacency matrix, respectively. We then employ the self-attention mechanism to capture long-range dependencies among features. To enhance interpretability, the node-selection pooling layers are designed to determine the importance of ROI for prediction. The proposed framework are applied to fMRI data of children (younger than 12 years old) from the Autism Brain Imaging Data Exchange datasets. Promising results demonstrate superior performance compared to recent similar studies. The obtained ROI detection results exhibit high correspondence with previous studies and offer good interpretability.

Mitochondria-associated endoplasmic reticulum membrane (MAM): a dark horse for diabetic cardiomyopathy treatment.

Diabetic cardiomyopathy (DCM), an important complication of diabetes mellitus (DM), is one of the most serious chronic heart diseases and has become a major cause of heart failure worldwide. At present, the pathogenesis of DCM is unclear, and there is still a lack of effective therapeutics. Previous studies have shown that the homeostasis of mitochondria and the endoplasmic reticulum (ER) play a core role in maintaining cardiovascular function, and structural and functional abnormalities in these organelles seriously impact the occurrence and development of various cardiovascular diseases, including DCM. The interplay between mitochondria and the ER is mediated by the mitochondria-associated ER membrane (MAM), which participates in regulating energy metabolism, calcium homeostasis, mitochondrial dynamics, autophagy, ER stress, inflammation, and other cellular processes. Recent studies have proven that MAM is closely related to the initiation and progression of DCM. In this study, we aim to summarize the recent research progress on MAM, elaborate on the key role of MAM in DCM, and discuss the potential of MAM as an important therapeutic target for DCM, thereby providing a theoretical reference for basic and clinical studies of DCM treatment.

Analyzing prehospital delays in recurrent acute ischemic stroke: Insights from interpretable machine learning.

OBJECTIVE: This study investigates prehospital delays in recurrent Acute Ischemic Stroke (AIS) patients, aiming to identify key factors contributing to these delays to inform effective interventions. METHODS: A retrospective cohort analysis of 1419 AIS patients in Shenzhen from December 2021 to August 2023 was performed. The study applied the Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP) for identifying determinants of delay. RESULTS: Living with others and lack of stroke knowledge emerged as significant risk factors for delayed hospital presentation in recurrent AIS patients. Key features impacting delay times included residential status, awareness of stroke symptoms, presence of conscious disturbance, diabetes mellitus awareness, physical weakness, mode of hospital presentation, type of stroke, and presence of coronary artery disease. CONCLUSION: Prehospital delays are similarly prevalent among both recurrent and first-time AIS patients, highlighting a pronounced knowledge gap in the former group. This discovery underscores the urgent need for enhanced stroke education and management. PRACTICE IMPLICATION: The similarity in prehospital delay patterns between recurrent and first-time AIS patients emphasizes the necessity for public health initiatives and tailored educational programs. These strategies aim to improve stroke response times and outcomes for all patients.

Are Different Versions of ChatGPT's Ability Comparable to the Clinical Diagnosis Presented in Case Reports? A Descriptive Study.

Objective: ChatGPT, an advanced language model developed by OpenAI, holds the opportunity to bring about a transformation in the processing of clinical decision-making within the realm of medicine. Despite the growing popularity of research related on ChatGPT, there is a paucity of research assessing its appropriateness for clinical decision support. Our study delved into ChatGPT's ability to respond in accordance with the diagnoses found in case reports, with the intention of serving as a reference for clinical decision-making. Methods: We included 147 case reports from the Chinese Medical Association Journal Database that generated primary and secondary diagnoses covering various diseases. Each question was independently posed three times to both GPT-3.5 and GPT-4.0, respectively. The results were analyzed regarding ChatGPT's mean scores and accuracy types. Results: GPT-4.0 displayed moderate accuracy in primary diagnoses. With the increasing number of input, a corresponding enhancement in the accuracy of ChatGPT's outputs became evident. Notably, autoimmune diseases comprised the largest proportion of case reports, and the mean score for primary diagnosis exhibited statistically significant differences in autoimmune diseases. Conclusion: Our finding suggested that the potential practicality in utilizing ChatGPT for clinical decision-making. To enhance the accuracy of ChatGPT, it is necessary to integrate it with the existing electronic health record system in the future.

Broadening horizons: the contribution of mitochondria-associated endoplasmic reticulum membrane (MAM) dysfunction in diabetic kidney disease.

Diabetic kidney disease (DKD) is a global health issue that presents a complex pathogenesis and limited treatment options. To provide guidance for precise therapies, it is crucial to accurately identify the pathogenesis of DKD. Several studies have recognized that mitochondrial and endoplasmic reticulum (ER) dysfunction are key drivers of the pathogenesis of DKD. The mitochondria-associated ER membrane (MAM) is a dynamic membrane contact site (MSC) that connects the ER and mitochondria and is essential in maintaining the normal function of the two organelles. MAM is involved in various cellular processes, including lipid synthesis and transport, calcium homeostasis, mitochondrial fusion and fission, and ER stress. Meanwhile, recent studies confirm that MAM plays a significant role in the pathogenesis of DKD by regulating glucose metabolism, lipid metabolism, inflammation, ER stress, mitochondrial fission and fusion, and autophagy. Herein, this review aims to provide a comprehensive summary of the physiological function of MAMs and their impact on the progression of DKD. Subsequently, we discuss the trend of pharmaceutical studies that target MAM resident proteins for treating DKD. Furthermore, we also explore the future development prospects of MAM in DKD research, thereby providing a new perspective for basic studies and clinical treatment of DKD.

Factors associated with loss to follow-up before and after treatment initiation among patients with tuberculosis: A 5-year observation in China.

Background: Loss to follow-up (LTFU) is a significant barrier to the completion of anti-tuberculosis (TB) treatment and a major predictor of TB-associated deaths. Currently, research on LTFU-related factors in China is both scarce and inconsistent. Methods: We collected information from the TB observation database of the National Clinical Research Center for Infectious Diseases. The data of all patients who were documented as LTFU were assessed retrospectively and compared with those of patients who were not LTFU. Descriptive epidemiology and multivariable logistic regression analyses were conducted to identify the factors associated with LTFU. Results: A total of 24,265 TB patients were included in the analysis. Of them, 3,046 were categorized as LTFU, including 678 who were lost before treatment initiation and 2,368 who were lost afterwards. The previous history of TB was independently associated with LTFU before treatment initiation. Having medical insurance, chronic hepatitis or cirrhosis, and providing an alternative contact were independent predictive factors for LTFU after treatment initiation. Conclusion: Loss to follow-up is frequent in the management of patients with TB and can be predicted using patients' treatment history, clinical characteristics, and socioeconomic factors. Our research illustrates the importance of early assessment and intervention after diagnosis. Targeted measures can improve patient engagement and ultimately treatment adherence, leading to better health outcomes and disease control.

Sesamol promotes browning of white adipocytes through liver-adipose crosstalk signal of hepatic fibroblast growth factor 21.

Sesamol (SEM), a lignan from sesame oil, exhibited potential benefits on obesity treatment by promoting browning of adipocytes, and the current study is aimed to explore the molecular mechanisms of SEM from the aspect of systemic liver-adipose crosstalk that mediated by hepatic fibroblast growth factor 21 (FGF21). Our in vivo data showed that SEM induced energy expenditure and white adipose tissue (WAT) browning by increasing the expression level of uncoupling protein-1 in high fat diet induced obese C57BL/6J mice. Elevated levels of circulating FGF21 associated with the increased expression of hepatic FGF21 were observed after SEM intervention. Simultaneously, the increased adipose fibroblast growth factor tyrosine kinase receptor 1/beta-klotho indicated that FGF21 sensitivity was enhanced by SEM in WAT. Furthermore, our in vitro results from HepG2 and 3T3-L1 cell lines confirmed the effects and revealed the mechanism of SEM on the white adipocytes browning. We found that with the specific inhibitors of PPARα, the SEM-mediated hepatic FGF21 expression was decreased, and with the specific inhibitors of PPARγ, the browning effect of adipocytes by SEM combining with FGF21 was significantly suppressed. Taken together, the mechanism of SEM for inducing the WAT browning might be the modulation of SEM on liver-adipose crosstalk mediated by FGF21, and the PPARs family might be the targets of SEM. The novel findings from the present study provided evidence that SEM could be a potent obesity-treating compound.

Anti-scaling performances of different aged landfill leachates on PPR and ABS pipe materials.

Landfill leachate, because of its complex components, may cause pipe clogging during its collection and transportation, and pose a threat to the environment. This study considers two typical drainage pipe materials, i.e., polypropylene-random (PPR) and acrylonitrile-butadiene-styrene (ABS), to discriminate their anti-scaling performances through a landfill leachate immersion experiment. The results show that both PPR and ABS pipe materials immersed in the younger-aged leachate are prone to scaling. The mass concentrations of Ca2+ in the leachate play a key role in scale formation, followed by Mg2+, pH, oxidation-reduction potential (ORP), dissolved oxygen (DO), and Cl-. In particular, Ca2+, pH, and DO show a positive relationship in scaling, while Mg2+ acts in a negative relationship. Calcium carbonate is the major constituent of the scales, accounting for 72.3% of the total. The scaling on the surface of the PPR pipe material is more serious than that of the ABS pipe material, revealed by the corresponding weight increment that is 33% higher than that related to ABS during the experimental period. It is thus indicated that ABS has better anti-scaling performance, which may be an optional material selected for the system design of leachate collection and transportation.

Ferroptosis: A new insight for treatment of acute kidney injury.

Acute kidney injury (AKI), one of the most prevalent clinical diseases with a high incidence rate worldwide, is characterized by a rapid deterioration of renal function and further triggers the accumulation of metabolic waste and toxins, leading to complications and dysfunction of other organs. Multiple pathogenic factors, such as rhabdomyolysis, infection, nephrotoxic medications, and ischemia-reperfusion injury, contribute to the onset and progression of AKI. However, the detailed mechanism remains unclear. Ferroptosis, a recently identified mechanism of nonapoptotic cell death, is iron-dependent and caused by lipid peroxide accumulation in cells. A variety of studies have demonstrated that ferroptosis plays a significant role in AKI development, in contrast to other forms of cell death, such as apoptosis, necroptosis, and pyroptosis. In this review, we systemically summarized the definition, primary biochemical mechanisms, key regulators and associated pharmacological research progress of ferroptosis in AKI. We further discussed its therapeutic potential for the prevention of AKI, in the hope of providing a useful reference for further basic and clinical studies.

Comprehensive transcriptome analysis reveals heat-responsive genes in flowering Chinese cabbage (Brassica campestris L. ssp. chinensis) using RNA sequencing.

Flowering Chinese cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee, 2n=20, AA) is a vegetable species in southern parts of China that faces high temperatures in the summer and winter seasons. While heat stress adversely impacts plant productivity and survival, the underlying molecular and biochemical causes are poorly understood. This study investigated the gene expression profiles of heat-sensitive (HS) '3T-6' and heat-tolerant (HT) 'Youlu-501' varieties of flowering Chinese cabbage in response to heat stress using RNA sequencing. Among the 37,958 genes expressed in leaves, 20,680 were differentially expressed genes (DEGs) at 1, 6, and 12 h, with 1,078 simultaneously expressed at all time points in both varieties. Hierarchical clustering analysis identified three clusters comprising 1,958, 556, and 591 down-regulated, up-regulated, and up- and/or down-regulated DEGs (3205 DEGs; 8.44%), which were significantly enriched in MAPK signaling, plant-pathogen interactions, plant hormone signal transduction, and brassinosteroid biosynthesis pathways and involved in stimulus, stress, growth, reproductive, and defense responses. Transcription factors, including MYB (12), NAC (13), WRKY (11), ERF (31), HSF (17), bHLH (16), and regulatory proteins such as PAL, CYP450, and photosystem II, played an essential role as effectors of homeostasis, kinases/phosphatases, and photosynthesis. Among 3205 DEGs, many previously reported genes underlying heat stress were also identified, e.g., BraWRKY25, BraHSP70, BraHSPB27, BraCYP71A23, BraPYL9, and BraA05g032350.3C. The genome-wide comparison of HS and HT provides a solid foundation for understanding the molecular mechanisms of heat tolerance in flowering Chinese cabbage.

Sleep quality and influencing factors and correlation with T-lymphocyte subpopulation counts in patients with pulmonary tuberculosis: a cross-sectional study.

BACKGROUND: Patients diagnosed with pulmonary tuberculosis (TB) have poor sleep quality due to multiple factors. We aimed to assess the sleep status and related factors of TB patients in Shenzhen, China. METHODS: A questionnaire survey was conducted on 461 TB patients hospitalized at Shenzhen Third People's Hospital from March 2021 to January 2022, and sleep quality was assessed using the Pittsburgh sleep quality index (PSQI). RESULTS: A total of 459 valid questionnaires were collected, and 238 of the 459 TB patients had general or poor sleep quality (PSQI > 5). Patients' gender, marriage, nutritional screening score, family atmosphere, fear of discrimination, fear of interactions, and the impact of the disease on their work life had significant effects on sleep quality (P < 0.05); PSQI scores of TB patients were negatively correlated with lymphocyte counts (r = - 0.296, P < 0.01), T-lymphocyte counts (r = - 0.293, P < 0.01), helper T lymphocyte counts (r = - 0.283, P < 0.01), killer T lymphocyte counts (r = - 0.182, P < 0.05), and were positively correlated with depression scores (r = 0.424, P < 0.01). Multivariable logistic regression analysis showed that male (OR = 1.64,95% CI 1.11-2.42, P < 0.05), unmarried (OR = 1.57, 95% CI 1.02-2.42, P < 0.05), NRS score grade 3(OR = 5.35, 95% CI 2.08-15.73, P < 0.01), general family atmosphere (OR = 2.23, 95% CI 1.07-4.93, P < 0.05), and the disease affecting work (OR = 1.66, 95% CI 1.11-2.50, P < 0.05) were factors influencing poor sleep quality. CONCLUSION: Most TB patients had varying degrees of sleep disturbance, which may be affected by their gender, marriage, family atmosphere, nutritional status, the effect of the disease on work life, and, depression, as well as lower absolute T-lymphocyte subpopulation counts. Appropriate interventions should be implemented to improve their sleep quality, when treating or caring for such patients.

Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients.

Objective: To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. Method: Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model's performance. The final model was shown by a website calculator. Result: Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879-0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. Conclusion: This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation.

Highly active repeat-mediated recombination in the mitogenome of the holoparasitic plant Aeginetia indica.

Mitogenomes of most flowering plants evolve slowly in sequence, but rapidly in structure. The rearrangements in structure are mainly caused by repeat-mediated recombination. However, patterns of repeat-mediated recombination vary substantially among plants, and to provide a comprehensive picture, characterization of repeat-mediated recombination should extend to more plant species, including parasitic plants with a distinct heterotrophic lifestyle. Here we assembled the mitogenome of the holoparasitic plant Aeginetia indica (Orobanchaceae) using Illumina sequencing reads. The mitogenome was assembled into a circular chromosome of 420,362 bp, 18,734 bp longer than that of another individual of A. indica which was assembled before as a linear molecule. Synteny analysis between the two mitogenomes revealed numerous rearrangements, unique regions of each individual and 0.2% sequence divergence in their syntenic regions. The A. indica mitogenome contains a gene content typical of flowering plants (33 protein-coding, 3 rRNA, and 17 tRNA genes). Repetitive sequences >30 bp in size totals 57,060 bp, representing 13.6% of the mitogenome. We examined recombination mediated by repeats >100 bp in size and found highly active recombination for all the repeats, including a very large repeat of ~16 kb. Recombination between these repeats can form much smaller subgenomic circular chromosomes, which may lead to rapid replication of mitochondrial DNA and thus be advantageous for A. indica with a parasitic lifestyle. In addition, unlike some other parasitic plants, A. indica shows no evidence for horizontal gene transfer of protein-coding genes in its mitogenome.