Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.

Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Cataract Development Among Pediatric Patients With Uveitis.

Importance: The long-term estimated risk of development of cataracts among pediatric patients with uveitis is not clear. Objective: To describe factors associated with the development of cataracts among pediatric patients with uveitis. Design, Setting, and Participants: This cohort study used the international TriNetX database to enroll pediatric patients with and without uveitis from January 1, 2002, to December 31, 2022. The nonuveitis cohort consisted of randomly selected control patients matched by age, sex, race and ethnicity, and specific comorbidities. Exposure: Diagnosis of uveitis, identified using diagnostic codes. Main Outcomes and Measures: The primary outcome was the risk of developing cataracts among the uveitis group compared with the nonuveitis comparison group, with hazard ratios (HRs) and 95% CIs reported. Results: A total of 22 687 pediatric patients with uveitis (mean [SD] age, 10.3 [5.6] years; 54.2% male) and 22 687 comparators without uveitis (mean [SD] age, 10.3 [5.6] years; 54.5% male) were enrolled in the study. The risk of cataracts was increased among pediatric patients with uveitis up to a follow-up duration of 20 years (HR, 17.17; 95%CI, 12.90-22.80) from the index date. Subgroup analyses revealed an elevated cataract risk across age groups: 0 to 6 years (HR, 19.09; 95% CI, 10.10-36.00), 7 to 12 years (HR, 27.16; 95% CI, 15.59-47.20), and 13 to 18 years (HR, 13.39; 95% CI, 8.84-20.30); both female sex (HR, 13.76; 95% CI, 9.60-19.71) and male sex (HR, 11.97; 95% CI, 8.47-16.91); and Asian (HR, 13.80; 95% CI, 3.28-58.07), Black or African American (HR, 10.41; 95% CI, 5.60-19.36), and White (HR, 15.82; 95% CI, 11.05-22.60) race. Furthermore, increased cataract risks were also observed among those with and without a history of immunosuppressive agents (with: HR, 26.52 [95% CI, 16.75-41.90]; without: HR, 17.69 [95% CI: 11.39-27.40]), a history of steroid eye drop use (with: HR, 29.51 [95% CI, 14.56-59.70]; without: HR, 16.49 [95% CI, 11.92-22.70]), and a history of intraocular procedures (with: HR, 11.07 [95%CI, 4.42-27.71]; without: HR, 14.49 [95% CI, 10.11-20.70]). Conclusions and Relevance: In this cohort study of pediatric patients with uveitis, an elevated risk of cataracts following a uveitis diagnosis was found compared with pediatric patients without uveitis. The findings suggest that pediatric patients with uveitis should be monitored for cataract development.

Do rheumatic diseases, long-term glucocorticoids, and immunosuppressant treatment, and vaccination impact the COVID-19 severity? Insight from a retrospective cohort study.

OBJECTIVES: The impact of rheumatic diseases, long-term medication, and vaccination on COVID-19 severity remain insufficiently understood, hindering effective patient management. This study aims to investigate factors influencing COVID-19 severity in Chinese rheumatic patients and to provide real-world evidence for patient care. METHODS: We conducted a retrospective observational study consisting of two cohorts, followed by a nested case-control analysis. The outpatient cohort included non-severe COVID-19 patients, while the inpatient cohort included consecutive severe COVID-19 inpatients. Additionally, rheumatic patients from both cohorts were included for the nested case-control study. Clinical information was obtained from electronic medical records and surveys. RESULTS: A total of 749 outpatients and 167 inpatients were enrolled. In the outpatient cohort, rheumatic diseases were identified as a risk factor for the severity of dyspnea (No rheumatic disease: OR = 0.577, 95% CI = 0.396-0.841, p = .004), but not for mortality, length of hospitalization, or hospitalization costs in the inpatient cohort. Long-term glucocorticoids use was identified as an independent risk factor for severity of dyspnea in rheumatic patients (OR = 1.814, 95% CI = 1.235-2.663, p = .002), while vaccination and immunosuppressant treatment showed no association. Vaccination was identified as a protective factor against hospitalization due to COVID-19 in patients with rheumatic diseases (OR = 0.031, 95% CI = 0.007-0.136, p < .001), whereas long-term glucocorticoids and immunosuppressant treatment showed no association. CONCLUSIONS: Rheumatic diseases and long-term glucocorticoids use are significant risk factors for COVID-19 severity in the Chinese population, whereas emphasizing the protective effects of vaccines against COVID-19 severity is crucial. Additionally, the investigation provides preliminary support for the concept that long-term immunosuppressant therapy does not necessarily require additional prescription adjustments.

Ilizarov technique for treating elbow stiffness caused by myositis ossificans: A case report.

BACKGROUND: Myositis ossificans (MO) is a rare disease involving the formation of bone outside the musculoskeletal system. While surgical intervention is the main treatment approach, preventing recurrence and standardized rehabilitation are also crucial. Here, we present a surgical strategy to prevent the recurrence of MO. CASE SUMMARY: A 28-year-old female patient was admitted for the first time for a comminuted fracture of the left olecranon. However, incorrect postoperative rehabilitation resulted in the development of elbow joint stiffness with ectopic ossification, causing a loss of normal range of motion. The patient was diagnosed with MO based on physical examination, X-ray findings, and clinical presentation. We devised a surgical strategy to remove MO, followed by fixation with an Ilizarov frame, and implemented a scientifically reasonable rehabilitation plan. The surgery lasted for 3 h with an estimated blood loss of 45 mL. A drainage tube was placed after surgery, and fluid was aspirated through ultrasound-guided puncture. The patient experienced a significant reduction in joint stiffness after surgery. In the final follow-up at 9 mouths, there was evident improvement in the range of motion of the elbow joint, and no other symptoms were reported. CONCLUSION: The Ilizarov frame is an advantageous surgical technique for facilitating rehabilitation after MO removal. It offers benefits such as passive recovery, individualized treatment, and prompt recovery.

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease.

BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. METHODS: This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. RESULTS: The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-ß-D-glucan test (G test). CONCLUSION: We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

Transformation behavior of hazardous jarosite into recyclable hematite in a solution with high concentrations of K+ and Na.

Iron in the leaching solution with high K+ and Na+ concentrations was usually precipitated as the typical hazardous and toxic jarosite residues. However, this method of treatment has been greatly restricted by increasingly strict environmental regulations. Here we propose that iron can be precipitated from the solution with high K+ and Na+ concentrations as recyclable hematite products by adjusting the concentration ratio of sodium and potassium ions in the solution. The transformation behavior of jarosite into hematite in high concentration potassium ion and sodium ion solution was explained based on collision theory. The results indicated that in instances where the concentration ratio of Na+/K+ is ≥ 4:1, the iron present in the solution can be effectively precipitated as a recyclable hematite product, as opposed to forming the conventional hazardous jarosite residue, even under conditions where the potassium ion concentration reaches levels as high as 4 g/L. On the other hand, thermodynamic and molecular dynamics simulations indicate that at a temperature of 185 °C, the decomposition transformation of Na-jarosite (32.64 kJ and 7.25 eV) is more energetically advantageous compared to that of K-jarosite (61.07 kJ and 15.31 eV). The results were verified by the leaching solution from smelting industry. The iron content in the residues is above 58%, the sulfur content is below 4%, the zinc content is below 1%, and the total iron concentration in the supernatant is about 4 g/L, reaching the production index of the smelting industry. The green, environmentally friendly, and recyclable separation of iron in a solution with high concentrations of potassium and sodium ions is achieved, which is of great significance for the treatment of iron-containing solution and wastewater in the chemical industry and metallurgy fields.

Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes.

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet's 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F's superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.

Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease.

OBJECTIVES: Predicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing. METHODS: We analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with 5-fold cross validation. RESULTS: Disease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the ß-diversity between groups (p=0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871. CONCLUSIONS: Identification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.

Hydrogels for Local and Sustained Delivery of Bacteriophages to Treat Wound Infections.

Bacteriophages (phages) are viruses that specifically target and kill bacteria, serving as a promising therapeutic to combat multidrug-resistant (MDR) pathogens such as Pseudomonas aeruginosa ( Pa ). However, delivering adequate concentrations of active phages directly to the infection site over sufficient times to eradicate infections remains an outstanding challenge to phage therapy (PT). Here we present "HydroPhage", a biocompatible hydrogel system for the sustained release of high-titre phages to effectively treat infections caused by MDR pathogens. We develop injectable hydrogels comprised of hyaluronic acid (HA) and polyethylene glycol (PEG) crosslinked through static covalent thioether bonds and hemithioacetal-based dynamic covalent crosslinks (DCC), which encapsulate phages at concentration up to 10 11 PFU per mL gel, and achieve sustained release over a week with more than 60% total phage recovery. In a preclinical mouse model of extended wound infection, we demonstrate enhanced bacterial clearance compared to intravenous treatment. Thus, using hydrogels for local and sustained delivery of phage may represent an effective approach to eradicating MDR infections broadly.

Implementation of a Smart Teaching and Assessment System for High-Quality Cardiopulmonary Resuscitation.

The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee's compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer's teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.

Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.

Understanding the Food Environment and Its Impact on Diet and Health in Asia: A Scoping Review.

Food environment (FE), an interface where people interact with a broader food system, is critical to health. Understanding the Asian FE may help to tackle the "triple burden of malnutrition" through informed research and policy. This review identifies FE domains assessed in the Asian context and collates the tools/measures used in these evaluations. We further synthesized the reported associations of FE with diet and health outcomes and identified knowledge gaps. Forty-two articles were reviewed (East Asia, n = 25, 60%; South Asia, n = 8, 19%; and Southeast Asia, n = 9, 21%). The results showed that FE was frequently examined in children, adolescents, or adults, but data were scarce in older adults. Food availability (n = 30) and accessibility (n = 19) were popularly studied domains. Furthermore, FE was measured using geographic information systems (n = 18), market (n = 7), or stakeholder (n = 21) surveys. Twenty-eight (67%) articles assessed associations of FE exposures with diet (n = 12) and health (n = 21). Increased food availability and accessibility were associated with poorer dietary and health outcomes despite nonexisting validity and reliability reporting in 62% of articles. Limited high-quality studies emphasize the need for harmonized definitions, better study designs, and validated FE measures/tools in Asia. Improving the quality of FE research is critical to designing effective interventions to improve public health nutrition in Asia.

Effect of Postoperative Prolonged sedation with Dexmedetomidine after successful reperfusion with Endovascular Thrombectomy on long-term prognosis in patients with acute ischemic stroke (PPDET): study protocol for a randomized controlled trial.

BACKGROUND: Endovascular thrombectomy (EVT) is a standard treatment for acute ischemic stroke (AIS) with large vessel occlusion. Hypertension and increased blood pressure variability within the first 24 h after successful reperfusion are related to a higher risk of symptomatic intracerebral hemorrhage and higher mortality. AIS patients might suffer from ischemia-reperfusion injury following reperfusion, especially within 24 h. Dexmedetomidine (DEX), a sedative commonly used in EVT, can stabilize hemodynamics by inhibiting the sympathetic nervous system and alleviate ischemia-reperfusion injury through anti-inflammatory and antioxidative properties. Postoperative prolonged sedation for 24 h with DEX might be a potential pharmacological approach to improve long-term prognosis after EVT. METHODS: This single-center, open-label, prospective, randomized controlled trial will include 368 patients. The ethics committee has approved the protocol. After successful reperfusion (modified thrombolysis in cerebral infarction scores 2b-3, indicating reperfusion of at least 50% of the affected vascular territory), participants are randomly assigned to the intervention or control group. In the intervention group, participants will receive 0.1~1.0 µg/kg/h DEX for 24 h. In the control group, participants will receive an equal dose of saline for 24 h. The primary outcome is the functional outcome at 90 days, measured with the categorical scale of the modified Rankin Scale, ranging from 0 (no symptoms) to 6 (death). The secondary outcome includes (1) the changes in stroke severity between admission and 24 h and 7 days after EVT, measured by the National Institute of Health Stroke Scale (ranging from 0 to 42, with higher scores indicating greater severity); (2) the changes in ischemic penumbra volume/infarct volume between admission and 7 days after EVT, measured by neuroimaging scan; (3) the length of ICU/hospital stay; and (4) adverse events and the all-cause mortality rate at 90 days. DISCUSSION: This randomized clinical trial is expected to verify the hypothesis that postoperative prolonged sedation with DEX after successful reperfusion may promote the long-term prognosis of patients with AIS and may reduce the related socio-economic burden. TRIAL REGISTRATION: ClinicalTrials.gov NCT04916197. Prospectively registered on 7 June 2021.

Study of chondrogenesis of umbilical cord mesenchymal stem cells in curdlan- poly(vinyl alcohol) composite hydrogels and its mechanical properties of freezing-thawing treatments.

The curdlan gel is a natural material produced by bacteria. It utilizes chemical cross-linking reactions to form a 3D porous composite hydrogel, increasing its porosity and water content, and improving its mechanical properties. It can be used in tissue repair and regenerative medicine. Curdlan-Poly(vinyl alcohol) (PVA) composite hydrogel can rapidly swell within 1 min due to its porous structure. Compression tests confirmed that it still maintains its original mechanical strength, even after five repeated freeze-thaw (FT) processes, making it suitable for long-term cryopreservation. The purpose of this study is to transplant umbilical cord mesenchymal stem cells (UC-MSCs) on Curdlan-PVA composite hydrogel and observe the chondrocytes on the material. The results of using 4',6-diamidino-2-phenylindole (DAPI), hematoxylin and eosin (H&E), calcein-acetoxymethyl ester (calcein AM), and Collagen type II-Fluorescein isothiocyanate (FITC) staining, confirmed that UC-MSCs can attach and differentiate into chondrocytes on 3D Curdlan-PVA composite hydrogel.

Impact of Immunomodulatory Therapy on COVID-19 Vaccine Response in Patients with Autoimmune Inflammatory Rheumatic Diseases.

Coronavirus disease 2019 (COVID-19) vaccination is essential for patients with autoimmune inflammatory rheumatic diseases (AIIRD) to reduce the risk of morbidity and mortality associated with serious COVID-19 infection. With endemicity, waning of vaccine- and infection-acquired immunity, and development of SARS-CoV-2 variants, the need for additional doses of vaccines against serious illness in high-risk immunocompromised persons remains imperative. This review examines how immunomodulatory therapies affect vaccine-induced immune response in patients with AIIRD. Glucocorticoids, methotrexate, azathioprine, calcineurin inhibitors, mycophenolate mofetil, tumor necrosis factor inhibitors, and abatacept have been shown to variably attenuate both humoral and cellular immune responses to vaccination. Janus kinase inhibitors reduce humoral immune response. In contrast, sulfasalazine, leflunomide, belimumab, interleukin (IL)-17, IL-12/23, IL-6, and IL-1 inhibitors appear favorable, with mild or no impact on vaccine response. Although rituximab is known to profoundly diminish humoral immune response, cellular immunity is relatively preserved. Administering a third and subsequent vaccine dose or temporally coordinating the dosing of immunomodulatory drugs may improve vaccine effectiveness. Further research is needed to personalise vaccination strategies for AIIRD patients, considering their specific immunomodulatory treatments.

[Effects of Exogenous Zinc on Growth and Root Architecture Classification of Maize Seedlings Under Cadmium Stress].

To explore the effects of different concentrations of zinc （Zn） on the growth and root architecture classification of maize seedlings under cadmium （Cd） stress， a hydroponic experiment was conducted to study the effects of different concentrations of Zn （0， 10， 25， 50， 100， 200， and 400 µmol·L-1） on the growth， root architecture and classification characteristics， Cd content， root Cd uptake capacity， and photosynthetic system of maize seedlings under Cd stress （50 µmol·L-1） by using Zhengdan 958 as the experimental material. Principal component analysis and the membership function method were used for comprehensive evaluation. The results showed that the 50 µmol·L-1 Cd stress had a significant toxic effect on maize seedlings， which significantly reduced chlorophyll content and photosynthetic parameters. The main root length， plant height， biomass， root forks， and root tips， including the root length and root surface area of the grade Ⅰ-Ⅲ diameter range and the root volume of the grade Ⅰ-Ⅱ diameter range， decreased significantly， which hindered the normal growth and development of maize seedlings. Compared with that under no Zn application， 100 µmol·L-1 and 200 µmol·L-1 Zn application reduced the uptake of Cd by maize seedlings， significantly reduced the Cd content in shoots and roots and the Cd uptake efficiency. The toxic effect on maize seedlings was alleviated， and the fresh weight， dry weight， tolerance index， and root forks of shoots and roots were significantly increased. The photosynthesis of maize seedlings was significantly enhanced， and the photosynthetic rate and the total chlorophyll content was significantly increased. The RL， SA， and RV in the Ⅰ-Ⅱ diameter range reached the maximum at 100 µmol·L-1 Zn， and the RL， SA， and RV in the Ⅲ diameter range reached the maximum at 200 µmol·L-1 Zn， which were significantly higher than those without Zn treatment. The comprehensive evaluation of the growth tolerance of maize seedlings showed that 100 µmol·L-1 and 200 µmol·L-1 Zn had better effects on alleviating Cd toxicity. Comprehensive analysis showed that the application of appropriate concentration of Zn could reduce the Cd content in maize seedlings， the Cd uptake capacity， and Cd uptake efficiency of roots； increase the biomass accumulation of maize seedlings； reduce the effect of Cd toxicity on root architecture； reduce the effect on the light and system； and improve the tolerance of maize seedlings to Cd.

Association between obstructive sleep apnea and low bone mass in adults: a systematic review and meta-analysis.

Introduction: This study aimed to synthesize existing evidence on the potential association between obstructive sleep apnea (OSA) and low bone mass in adults. Methods: Electronic searches of four main databases were performed. The inclusion criteria consisted of observational studies investigating the relationship between OSA and bone mass, osteoporosis, fractures, or bone metabolism markers in adult population. Bone mineral density (BMD) and T score of lumbar and femur neck, incidence of osteoporosis and fractures, bone metabolism marker levels were extracted as primary outcomes. Results: Among the 693 relevant publications, 10 studies consisting of 158,427 participants met with the inclusion and exclusion criteria. Meta-analysis showed a significant lower BMD of lumbar (mean difference (MD) = - 0.03; 95% CI - 0.05, - 0.01; I2 = 46%), femur neck (MD = - 0.06; 95% CI - 0.12, 0.00; I2 = 71%), and a significant lower T score of lumbar (MD = - 0.42; 95% CI - 0.79, - 0.05; I2 = 63%) in the OSA group. The results suggested that both male (odds ratio (OR) = 2.03; 95% CI 1.23, 3.35; I2 = 38%) and female (OR = 2.56; 95% CI 1.96, 3.34; I2 = 0%) had higher risk of osteoporosis in the OSA group. Besides, meta-analysis also showed that bone-specific alkaline phosphatase was significantly lower in OSA patients (MD = - 1.90; 95% CI - 3.48, - 0.32; I2 = 48%). Conclusions: A potential association between OSA and lower bone mass in adults is preliminarily proved. It also seems plausible that both male and female with OSA have a higher risk of osteoporosis. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-023-00481-1.