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Abstract Background The aim of this retrospective study was to examine the risk factors of positive lower respiratory tract cultures and to investigate whether nosocomial infections are common in patients with positive lower respiratory tract cultures. Methods We enrolled 86 patients diagnosed with influenza A-related critical illness who were treated at Fuzhou Pulmonary Hospital of Fujian in China between 1st October 2013 and 31st March 2019. The of admission were used to divide the enrolled patients into two groups. Sputum and bronchoalveolar lavage fluid specimens were collected within 48 h after admission for culture. All samples were cultured immediately after sampling. Nosocomial infections are defined as any symptom or sign of pulmonary infiltration, confirmed by X-ray, after 5 days of admission and positive results from one or more cultures. Results The average age of this cohort was (54.13 ± 16.52) years. Based on the culture results, Staphylococcus aureus and Candida albicans had the highest positive rates (3.40% (3/86) and 20.90% (18/86), respectively). In patients with positive lower respiratory tract cultures, the incidence of nosocomial infection was 73.30% (22/30) five days after admission. However, the incidence of nosocomial infection was lower (42.80%, 24/56) in patients with negative lower respiratory tract cultures. Hemoptysis, systolic pressure at admission, and blood urea nitrogen level at admission were all independent risk factors for positive lower respiratory tract cultures within 48 h of admission. Conclusion Our data showed that a significant proportion of patients with pneumonia exhibited co-infections with bacteria or fungi within five days of hospital admission. Hemoptysis, systolic pressure, and blood urea nitrogen levels at admission emerged as the key risk factors. These findings underscore the necessity of closely monitoring patients with influenza infection, particularly for positive bacterial or fungal cultures within the initial 48 h of admission.

ObjectiveTo evaluate the diagnostic value of metagenomic next-generation sequencing (mNGS) in pulmonary cryptococcosis (PC) using bronchoalveolar lavage fluid (BALF) and lung biopsy tissue specimens.MethodsIn this retrospective study, 321 patients diagnosed with lower respiratory tract diseases who underwent mNGS using BALF and LBT samples, between January 2021 and December 2023 were included. Individuals were classified into PC and non-PC groups according to the diagnostic criteria for PC, and conventional fungal cultures were performed. A serum/BALF cryptococcal antigen (CrAg) test was performed in some patients with PC. The diagnostic efficiencies of three methods for PC (mNGS, conventional culture, and CrAg) were compared. Additionally, two mNGS methods were used in this study: original mNGS (OmNGS, testing time from January 2021 to December 2022) and modified mNGS (MmNGS, testing time from January to December 2023). The diagnostic efficiency of the two mNGS methods on PC was simultaneously compared.ResultsAmong the 321 patients, 23 (7.2%) had PC and 298 (92.8%) did not. Compared with the composite reference standard for PC diagnosis, the sensitivity, specificity, and accuracy of mNGS for PC were 78.3% (95% confidence interval [CI], 55.8%–91.7%), 98.7% (95% CI, 96.4%–99.6%), and 97.2% (95% CI, 94.7%–98.7%), respectively. The sensitivity of mNGS was similar to that of CrAg (80.0%, 12/15) (P > 0.05). The diagnostic sensitivity of both mNGS and CrAg was higher than that of conventional culture (35.0%, 7/20) (P = 0.006, P = 0.016), and the combined detection of mNGS and CrAg further improved the diagnostic sensitivity of PC (93.3%, 14/15). The area under the receiver operating characteristic curve of mNGS was superior to that of conventional culture (0.885 vs. 0.675). In addition, the diagnostic sensitivity of PC was higher than that of OmNGS (P = 0.046).ConclusionThe sensitivity of mNGS is better than that of conventional culture. The combination of mNGS and CrAg improves the testing sensitivity of Cryptococcus. MmNGS could further improve the detection of Cryptococcus. Conventional PC detection methods are indispensable and mNGS can be used as a rapid and accurate auxiliary diagnostic method for PC.

Background: Amnestic mild cognitive impairment (aMCI) is an important window of opportunity for early intervention and rehabilitation in dementia. The aim of this study was to investigate the feasibility and effect of delivering transcutaneous electrical acupuncture stimulation (TEAS) intervention to elders with aMCI. Methods: A total of 61 aMCI patients were randomly allocated into the intervention group (receiving a 12-week TEAS) and control group (receiving health education). The feasibility outcomes included recruitment rate, retention rate, adherence rate, and an exploration of patients’ views and suggestions on the research. The effective outcomes included cognitive function, sleep quality, and life quality, which were measured by the Montreal cognitive assessment scale (MoCA), auditory verbal learning test—Huashan version (AVLT-H), Pittsburgh sleep quality index (PSQI), and quality of life short-term-12 (QoL SF-12). Results: The recruitment rate, retention rate, and adherence rate were 67.35%, 92.42%, and 85.29%, respectively. Most aspects of the research design and administration of the TEAS intervention were acceptable. The quantitative analysis suggests that compared with the control group, the scores of MoCA, AVLT-H, and SF-12 (mental component summary) were significantly better (p < 0.05); however, the differences were not statistically significant in PSQI and SF-12 (physical component summary) (p > 0.05). Conclusions: The findings demonstrated that the study was feasible. TEAS awas possible for enhancing cognitive function and mental health in people with aMCI.

Abstract Background Highly emetogenic chemotherapy induces emesis in cancer patients without prophylaxis. The purpose of this study was to evaluate the efficacy and safety of a fosaprepitant‐based triple antiemetic regimen for the prevention of chemotherapy‐induced nausea and vomiting (CINV) in patients with solid malignant tumors, determine risk factors and externally validate different personalized risk models for CINV. Methods This phase III trial was designed to test the non‐inferiority of fosaprepitant toward aprepitant in cancer patients who were to receive the first cycle of single‐day cisplatin chemotherapy. The primary endpoint was complete response (CR) during the overall phase (OP) with a non‐inferiority margin of 10.0%. Logistic regression models were used to assess the risk factors of CR and no nausea. To validate the personalized risk models, the accuracy of the risk scoring systems was determined by measuring the specificity, sensitivity and area under the receiver operating characteristic (ROC) curve (AUC), while the predictive accuracy of the nomogram was measured using concordance index (C‐index). Results A total of 720 patients were randomly assigned. CR during the OP in the fosaprepitant group was not inferior to that in the aprepitant group (78.1% vs. 77.7%, P = 0.765) with a between‐group difference of 0.4% (95% CI, ‐5.7% to 6.6%). Female sex, higher cisplatin dose (≥ 70 mg/m2), no history of drinking and larger body surface area (BSA) were significantly associated with nausea. The AUC for the acute and delayed CINV risk indexes was 0.68 (95% CI: 0.66‐0.71) and 0.66 (95% CI: 0.61‐0.70), respectively, and the C‐index for nomogram CINV prediction was 0.59 (95% CI, 0.54‐0.64). Using appropriate cutoff points, the three models could stratify patients with high‐ or low‐risk CINV. No nausea and CR rate were significantly higher in the low‐risk group than in the high‐risk group (P < 0.001). Conclusions Fosaprepitant‐based triple prophylaxis demonstrated non‐inferior control for preventing CINV in patients treated with cisplatin‐base chemotherapy. Female cancer patients without a history of alcohol consumption, with larger BSA and received high‐dose cisplatin might be more vulnerable to CINV. Three personalized prediction models were well‐validated and could be used to optimize antiemetic therapy for individual patients.

Abstract Background Disseminated cryptococcal infection is especially prone to occur in immunosuppressed hosts. We herein report the case of an immunosuppressed girl with disseminated cryptococcal infection in whom pulmonary cryptococcosis (PC) presented as diffuse cavitary pulmonary nodules, a finding which has rarely been reported. Case presentation A 16-year-old immunocompromised girl presented with fever and a non-productive cough. A chest computed tomography (CT) scan revealed diffuse pulmonary nodules with cavities. Subsequent results were consistent with disseminated cryptococcosis with Cryptococcus identified in her blood, bone marrow and cerebrospinal fluid cultures. Thus, the patient was diagnosed with disseminated cryptococcal infection with PC, cryptococcus meningitis, cryptococcus osteomyelitis and cryptococcus sepsis. After antifungal treatment, the patient demonstrated both clinical and chest radiological improvement. Conclusion The atypical clinical manifestations of a disseminated cryptococcal infection and the rare manner of chest CT findings of PC reported in our case are easy to misdiagnose. It is necessary to carry out a thorough search for a definitive diagnosis using various methods.

BackgroundMeasurements of IgG antibodies to wild-type SARS-CoV-2 antigens can assess vaccine efficacy, but the absolute risk of Omicron symptomatic infection at different IgG levels for children and adolescents remains uncertain, as well as the minimum effective antibody level. We sought to determine the relationship between the tertiles of IgG antibodies to wild-type SARS-CoV-2 antigens and children with symptomatic infection of the pandemic and duration to negative conversion in China for the first time.MethodsA retrospective study was conducted, including 168 participants under 18 years old from the No.2 People’s Hospital of Lanzhou, China, diagnosed with Omicron variant BA.2.38 between July 8, 2022, and August 2, 2022. We calculated odds ratios (OR) in univariate and multivariate regression to assess the association of symptomatic infection with the tertiles of IgG, respectively. Kaplan–Meier curves and Cox proportional hazards regression were used to evaluate the relationship between IgG level and negative conversion time.ResultsThe average age of the 168 children included in this study was 7.2 (4.7) years old, 133 (79.2%) were symptomatic patients, and the average negative conversion time was 12.2 (3.5) days. The participants with high IgG levels were less likely to become symptomatic, had a shorter turnaround time, and had higher values of IgM and nucleic acid CT. Compared to those with the lowest tertile of IgG, patients with the highest tertile had a 91% lower risk of developing a symptomatic infection after fully adjusting for confounders (OR = 0.09, 95% CI, 0.02-0.36, p = 0.001). There’s no robust relationship between IgG level and negative conversion time in multivariate Cox regression.ConclusionThe risk of developing a symptomatic infection can be predicted independently by tertiles of IgG antibodies to wild-type SARS-CoV-2 antigens. High IgG levels can inhibit viral replication, vastly reduce the risk of symptomatic infections and promote a virus-negative conversion, especially when IgG quantitative detection was ≥3.44 S/CO, a potential threshold for protection and booster strategy in the future. More data and research are needed in the future to validate the predictive models.

Postmenopausal osteoporosis is the main cause of fractures in women. Resistance exercise has a positive effect on bone mineral density in postmenopausal osteoporosis patients, but its mechanism is unclear. The purpose of this study was to explore the mechanism of resistance exercise in improving ovariectomized osteoporotic rats based on the transcriptome sequencing technique. Eighteen female Sprague-Dawley rats were randomly divided into the sham-operated group, the non-exercise group, and the resistance exercise group. The rat model of postmenopausal osteoporosis was established by bilateral ovariectomy. Ten weeks after the operation, the resistance exercise group received 2 weeks of adaptive training, and 12 weeks of resistance exercise began in the 13th week. The rats were trained 5 days per week, in 4 sets of 3 repetitions per day. After the intervention, all rats were sacrificed, and the body weight, bone mineral density, trabecular bone microarchitecture, and bone biomechanics were examined. At the same time, RNA-seq and enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes were performed on the left tibias, followed by Elisa and RT-qPCR verification. It had been found that resistance exercise can effectively counteract the weight gain of ovariectomized osteoporotic rats, and has a good effect on bone mineral density and trabecular bone microarchitecture. Enrichment analysis showed that regulation of gene expression and osteoclast differentiation is the most closely related biological process and signaling pathway shared by RE/Ovx and NE/Ovx groups. Our results revealed that resistance exercise can play a role in inhibiting osteoclast activation and preventing the enhancement of osteoclast bone resorption function in ovariectomized osteoporotic rats by inhibiting Fos/Fosb-regulated TRAP activation and relieving Calcr inhibition, which has important application value in preventing bone loss caused by estrogen deficiency.

Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a health concern for both unvaccinated and vaccinated individuals against coronavirus disease 2019 (COVID-19). To date, the humoral immune response following vaccination and natural infection remains uncharacterized in children ages 17 years and younger. To address this concern, we performed clinical and immunological analyses of IgM and IgG antibody responses to SARS-CoV-2 Omicron BA.2.38 infection in 64 pediatric patients. COVID-19 symptom severity decreased with age in pediatric patients, from 70.8% (17/24) in patients 0–2 years of age to 50% (6/12) and 50% (14/28) in patients 3–5 years and 6–17 years of age, respectively. Furthermore, fewer patients experienced symptoms when vaccinated with the CoronaVac or BBIBP-CorV vaccine (50%, 13/26) than unvaccinated patients (71%, 22/31). Using a protein array, we found that the Omicron BA.2.38 infection induced antibody responses to other Omicron variants (Omicron BA.1-BA.5), which increased with vaccination. Notably, non-Omicron and Omicron variants showed distinct serotypes. Altogether, our results provide insight into the clinical and immunological characteristics of pediatric patients with COVID-19 Omicron BA.2.38 who have and have not been vaccinated against COVID-19. These data may help develop more effective diagnostic tests and vaccines in the future.

Background: Alzheimer's disease (AD) is a chronic and irreversible neurodegenerative disease. Oxidative stress emerges at the early AD stage. As a non-invasive therapy with few adverse reactions, transcutaneous electrical acupoint stimulation (TEAS) combines acupuncture points of traditional Chinese medicine (TCM) and electrical stimulation. This study aimed to investigate the amelioration effects of preventive TEAS treatment (P-TEAS) on cognitive impairment and oxidative stress in AD model rats. Methods: The AD model was established via subcutaneous injections of D-galactose (D-gal, 120 mg/kg/d) into the back of neck for 9 weeks in Sprague Dawley (SD) rats to simulate the oxidative stress in the early AD stage. On the first day of the 10th week, Aβ1–42 (1 μg/μl) was injected into the CA1 regions of the bilateral hippocampus. P-TEAS was synchronized from the first day of subcutaneous D-gal injections for 9 weeks. Results: Empirical measurements showed that P-TEAS can improve the spatial memory ability of AD model rats in the Morris water maze. Superoxide dismutase (SOD) was upregulated in the P-TEAS group. Through the detection of the anti-oxidative stress signaling pathway, namely, Kelch-like ECH-associated protein 1 (Keap1)/ NFE2-related factor 2 (Nrf2), it was found that P-TEAS could promote Nrf2 entering into the nucleus and upregulating the production of protective factors heme oxygenase 1 (HO-1) and NADPH quinone oxidoreductase 1 (NQO1). It was also found that P-TEAS could downregulate the expressions of BCL2-associated X-protein (Bax), caspase 3, and caspase 9 to inhibit neuronal apoptosis. Conclusions: P-TEAS has similar efficacy to electroacupuncture in preventing AD occurrence and development. P-TEAS is a new non-invasive intervention therapy for the prevention of AD.

Abstract Background Rheumatoid arthritis (RA) can cause severe physical impairment and a reduced quality of life, and there is limited evidence for any effective intervention. Aerobic exercise may be beneficial for improving symptoms. Therefore, the purpose of this meta-analysis was to evaluate the effectiveness and safety of aerobic exercise for rheumatoid arthritis patients. Methods PubMed, The Cochrane Library, Web of Science, EMBASE, CNKI, WanFang Data and VIP databases were searched. Randomized controlled trials of the effectiveness and safety of aerobic exercise for rheumatoid arthritis were included. Risks of bias were assessed by two independent reviewers using the methods described in the RevMan 5.3, GRADEpro and the Cochrane Handbook. Meta-analyses were performed to investigate the effects of aerobic exercise on rheumatoid arthritis. Results A total of 13 RCTs were included, including 967 rheumatoid arthritis patients. The Meta-analysis results showed that aerobic exercise can improve functional ability [MD = − 0.25, 95% CI (− 0.38, − 0.11), P = 0.0002], relieve pain [SMD = − 0.46, 95% CI (− 0.90, − 0.01), P = 0.04], increase aerobic capacity [MD = 2.41, 95% CI (1.36, 3.45), P

Abstract Background Based on more than 15 million follow-up records of 404,426 patients from Guangdong Mental Health Center over the past 10 years, this study aims to propose a disease risk analysis and prediction model to support chronic disease management and clinical research for schizophrenia patients. Methods Based on a mental health information and intelligent data processing platform, we design an automatic AHP framework called AutoAHP to analyze and predict the disease risks of schizophrenia patients. Through automatic extraction, transformation and integration of follow-up data in the real world such as demography, treatment, and the disease course, a chronic database of patient status is established. In combination with age-period-cohort, logistic regression and Cox models, we apply the AutoAHP to assess disease risk and implement risk prediction in practice. Results A list of essential factors for risk prediction are identified, including annual changes in mental health policy, public support, regional difference, patient gender, compliance, and social function. After the verification of 1,222,038 complete disease course and treatment records of 256,050 patients, the AutoAHP framework achieves a precision of 0.923, a recall of 0.924, and a F1 of 0.923. The model is demonstrated to be superior to general models and has better performance in risk prediction. Conclusions Aiming at the risk assessment of patients with schizophrenia which is influenced by factors, such as time, region and complication, the AutoAHP framework is able to be applied as a model in combination with logistic regression and Cox models to support clinical analysis of disease risk related factors and assist decision-making in chronic disease management.

Objectives. Appropriate mechanical ventilation may change the prognosis of patients with viral pneumonia-associated acute respiratory distress syndrome (ARDS). This study aimed to identify the factors associated with the success of noninvasive ventilation in the management of patients with ARDS secondary to respiratory viral infection. Methods. In this retrospective cohort study, all patients with viral pneumonia-associated ARDS were divided into the noninvasive mechanical ventilation (NIV) success group and the NIV failure group. The demographic and clinical data of all patients were collected. The factors associated with the success of noninvasive ventilation were identified by the logistic regression analysis. Results. Among this cohort, 24 patients with an average age of 57.9 ± 17.0 years received successful NIVs, and NIV failure occurred in 21 patients with an average age of 54.1 ± 14.0 years. The independent influencing factors for the success of the NIV were the acute physiology and chronic health evaluation (APACHE) II score (odds ratio (OR): 1.83, 95% confidence interval (CI): 1.10–3.03) and lactate dehydrogenase (LDH) (OR: 1.011, 95% CI: 1.00–1.02). When the oxygenation index (OI) is 19, and LDH > 498 U/L, the sensitivity and specificity of predicting a failed NIV were (66.6% (95% CI: 43.0%–85.4%) and 87.5% (95% CI: 67.6%–97.3%)); (85.7% (95% CI: 63.7%–97.0%) and 79.1% (95% CI: 57.8%–92.9%)); (90.4% (95% CI: 69.6%–98.8%) and 62.5% (95% CI: 40.6%–81.2%)), respectively. The areas under the receiver operating characteristic curve (AUC) of the OI, APACHE II scores, and LDH were 0.85, which was lower than the AUC of the OI combined with LDH and the APACHE II score (OLA) of 0.97 (P=0.0247). Conclusions. Overall, patients with viral pneumonia-associated ARDS receiving successful NIV have lower mortality rates than those for whom NIV failed. In patients with influenza A-associated ARDS, the OI may not be the only indicator of whether NIV can be used; a new indicator of NIV success may be the OLA.

BackgroundMigraine is a chronic neurological disease causing significant socioeconomic burden and impaired quality of life. Chinese medicine is commonly used for migraine in China. Clinical trials have generated evidence of the effectiveness of Chinese medicine therapies for migraine. However, little is known about how to use these therapies to treat migraine in real-world clinical settings.MethodsIn this retrospective study, we analyzed data from the electronic medical records (EMRs) of 2,023 migraine patients who attended the Guangdong Provincial Hospital of Chinese Medicine (GPHCM) between July 2018 and July 2020.ResultsMore than three-quarters (77.21%) of the patients were female. Most (78.20%) of the patients were aged between 18 and 50 years, 18.49% were aged above 50 years, and the remaining 3.31% were under 18 years. Sleep disorders were the most documented comorbidity occurring in 27.29% of patients, and more common in females (29.77%) than male (18.87%). Fatigue was the most frequently reported trigger of migraine attacks among all patients (9.39%), while menstruation was the most common trigger for female patients (10.24%). Less than a quarter of patients (21.01%) reported a history of taking analgesic medication for their migraine. The median treatment duration reported by the patients was 10 days. Chinese herbal medicine (CHM) was the predominant treatment for migraine at the hospital (88.48%), while pharmacotherapies were prescribed to 28.97% of the patients. CHM was prescribed more often as a sole treatment (53.58% of patients) than combined with pharmacotherapies (27.39% of patients). Among patients who reported improvements after taking CHM, the most frequently used herbs were fu ling and chuan xiong, the most frequent patented CHM product was tong tian oral solution, and the main herbal formulae were chuan xiong cha tiao san and yi qi cong ming tang.ConclusionCHM formulae, such as chuan xiong cha tiao san and yi qi cong ming tang, patented CHM product tong tian oral solution, and some herbs are potentially effective treatments for migraine. As such, CHM can be used as an alternative to conventional pharmacotherapies for migraine and is worth further evaluation in randomized controlled trials.

BackgroundKnowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM). ObjectiveThis paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM. MethodsA new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task. ResultsExperiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models. ConclusionsExperiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference–based medical question answering.

Abstract Background With China experiencing unprecedented economic development and social change over the past three decades, Chinese policy makers and health care professionals have come to view mental health as an important outcome to monitor. Our study conducted an epidemiological study of psychosis in Guangdong province, with 20 million real-world follow-up records in the last decade. Methods Data was collected from Guangdong mental health information platform from 2010 to 2019, which had standardized disease registration and follow-up management for nearly 600,000 patients with six categories of mental diseases and 400,000 patients with schizophrenia. We conducted clinical staging for the disease course of the patients and divided the data with various factors into different stages of disease. Quantitative analysis was utilized to investigate the high relevant indicators to the disease. The results were projected on geography map for regional distribution analysis. Results The majority cases of mental disease incidence were between the age of 15 and 29, while the peak age for both male and female was between 20 to 24 years old. The disease course with the largest number of patients’ cases was between 5 to 10 years. The therapeutic effect of patients gradually decreased with the development of disease course, while the risk increased with the disease course. The analysis of influencing factors showed that poor economic conditions incurred higher risk scores, and good medication adherence was effective in improving treatment outcomes. In addition, receiving good education contributed to the reduction of the risk of schizophrenia and the improvement of the efficiency of early treatment. Through the analysis of regional distribution of schizophrenia disease, developed economic conditions and favorable resource conditions could promote the reduction of disease risk, while in economically backward regions, it often accompanied with lower therapeutic effect and higher disease risk. Conclusions Certain demographic factors had a relatively prominent impact on the therapeutic effect and risk of schizophrenia, such as high-quality medication adherence. Therapeutic effect and risk were highly correlated. Backward economic conditions often associated with poor efficacy and higher risk assessment, and the developed economy and better medical resource are beneficial for the treatment of psychotic.

Background: Post-traumatic stress disorder (PTSD) is the most common psychiatric sequelae among novel coronavirus disease (COVID-19) patients. The aim of this study was to determine the prevalence of PTSD symptoms, PTSD-related factors, and its relationship with quality of life at long-term follow-up in hospitalized COVID-19 survivors.Methods: A cross-sectional study was undertaken to evaluate the health consequences of hospitalized COVID-19 survivors. All participants were interviewed face-to-face through a series of questionnaires: a researcher-developed symptom questionnaire, the Post-traumatic Stress Disorder Checklist–Civilian Version, the Generalized Anxiety Disorder 7-item, and the 36-item Short Form.Results: A total of 574 participants were enrolled with an average age of 57 years. The median follow-up time post-discharge was 193.9 days (SD = 15.32). Among the participants, 77.9% of survivors presented with at least one symptom, where fatigue or muscle weakness (47.9%) was reported the most frequently, followed by chest distress (29.4%) and sleep difficulty (29.4%). The prevalence of PTSD was 11.15% [95% confidence interval (CI): 8.56, 13.73] with a cut-off score of 44. Factors such as respiratory symptoms [odds ratio (OR): 3.53; 95% CI: 1.68–7.42], anxiety (OR: 14.64; 95% CI: 7.09–30.21), and sleep difficulty (OR: 2.17; 95% CI: 1.14–4.16) were positively related to PTSD. Those COVID-19 survivors with potential PTSD had significantly lower quality of life than those without (P < 0.05).Conclusion: Our study illustrated that a significant number of COVID-19 survivors were suffering from physical or mental distress to varying degrees at 6 months post-discharge. People with PTSD were more likely to experience persistent respiratory symptoms and sleep difficulty, as well as anxiety and a decreased quality of life. Such survivors require greater attention to their mental health, particularly the PTSD symptoms at the early phase, which may play an important role in the recovery of both the physical and psychological health of COVID-19 survivors.

Background and Purpose: Coronavirus disease 2019 (COVID-19) rapidly resulted in a pandemic. Information on patients with a history of cerebrovascular disease (CVD) infected with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is limited. This study investigated the clinical features and the risk factors of developing adverse outcomes in patients with COVID-19 and with previous CVD.Methods: This was a single-center retrospective clinical study including all the confirmed cases of COVID-19 at Wuhan Huoshenshan Hospital from February 4 to April 7, 2020. Differences in clinical characteristics were compared between patients with and without a history of CVD. The incidences of severe events comprising all-cause death, intensive care unit admission, shock, and mechanical ventilation usage during hospitalization in two groups were compared using propensity score matching analysis and multivariate logistic regression analyses. Besides, the risk factors of developing severe events in patients with COVID-19 who also have history of CVD were analyzed.Results: A total of 2,554 consecutive patients were included in our study, of whom 109 (4.27%) had a medical history of CVD. Patients with CVD tend to be older and with more comorbidities, including hypertension, diabetes, coronary heart disease, and chronic obstructive pulmonary disease. The levels of white blood cell, neutrophil, C-reactive protein, creatine kinase isoenzymes, and lactate dehydrogenase were higher, whereas the levels of lymphocyte and albumin were lower in the CVD group. Compared to those without CVD, patients with CVD were more likely to have severe events after age matching (12.8 vs. 5.7%, P = 0.012). After adjusting for the confounding effects of age, sex, smoking, and comorbidities, the odds ratio for developing severe events with a history of CVD was 2.326 (95% CI, 1.168–4.630; P = 0.016). Besides, patients with CVD, either with decreased lymphocyte count (OR 9.192, 95% CI, 1.410–59.902, P = 0.020) or increased blood urea nitrogen (OR 5.916, 95% CI, 1.072–32.641, P = 0.041), had a higher risk of developing severe events during hospitalization.Conclusions: Patients with CVD history tend to have adverse clinical outcomes after being infected with SARS-COV-2. Decreased lymphocyte counts and increased blood urea nitrogen levels may be risk factors for adverse outcomes in patients with COVID-19, and had CVD.

With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".

Background: Real-world evidence on the effectiveness of inactivated vaccines against the Delta and Omicron (BA.2.38) variants remains scarce. Methods: A retrospective cohort study was conducted to estimate the adjusted vaccine effectiveness (aVE) of one, two, and three doses of inactivated vaccines in attenuating pneumonia, severe COVID-19, and the duration of viral shedding in Delta and Omicron cases using modified Poisson and linear regression as appropriate. Results: A total of 561 COVID-19 cases were included (59.2% Delta and 40.8% Omicron). In total, 56.4% (184) of Delta and 12.0% (27) of Omicron cases had COVID-19 pneumonia. In the two-dose vaccinated population, 1.4% of Delta and 89.1% of Omicron cases were vaccinated for more than 6 months. In Delta cases, the two-dose aVE was 52% (95% confidence interval, 39–63%) against pneumonia and 61% (15%, 82%) against severe disease. Two-dose vaccination reduced the duration of viral shedding in Delta cases, but not in booster-vaccinated Omicron cases. In Omicron cases, three-dose aVE was 68% (18%, 88%) effective against pneumonia, while two-dose vaccination was insufficient for Omicron. E-values were calculated, and the E-values confirmed the robustness of our findings. Conclusions: In Delta cases, two-dose vaccination within 6 months reduced pneumonia, disease severity, and the duration of viral shedding. Booster vaccination provided a high level of protection against pneumonia with Omicron and should be prioritized.

Abstract Rationale Our pilot study suggested that noninvasive ventilation (NIV) reduced the need for intubation compared with conventional administration of oxygen on patients with “early” stage of mild acute respiratory distress syndrome (ARDS, PaO2/FIO2 between 200 and 300). Objectives To evaluate whether early NIV can reduce the need for invasive ventilation in patients with pneumonia-induced early mild ARDS. Methods Prospective, multicenter, randomized controlled trial (RCT) of NIV compared with conventional administration of oxygen through a Venturi mask. Primary outcome included the numbers of patients who met the intubation criteria. Results Two hundred subjects were randomized to NIV (n = 102) or control (n = 98) groups from 21 centers. Baseline characteristics were similar in the two groups. In the NIV group, PaO2/FIO2 became significantly higher than in the control group at 2 h after randomization and remained stable for the first 72 h. NIV did not decrease the proportion of patients requiring intubation than in the control group (11/102 vs. 9/98, 10.8% vs. 9.2%, p = 0.706). The ICU mortality was similar in the two groups (7/102 vs. 7/98, 4.9% vs. 3.1%, p = 0.721). Multivariate analysis showed minute ventilation greater than 11 L/min at 48 h was the independent risk factor for NIV failure (OR, 1.176 [95% CI, 1.005–1.379], p = 0.043). Conclusions Treatment with NIV did not reduce the need for intubation among patients with pneumonia-induced early mild ARDS, despite the improved PaO2/FIO2 observed with NIV compared with standard oxygen therapy. High minute ventilation may predict NIV failure. Trial registration NCT01581229. Registered 19 April 2012

Abstract Background Gender information frequently exists in the eligibility criteria of clinical trial text as essential information for participant population recruitment. Particularly, current eligibility criteria text contains the incompleteness and ambiguity issues in expressing transgender population, leading to difficulties or even failure of transgender population recruitment in clinical trial studies. Methods A new gender model is proposed for providing comprehensive transgender requirement specification. In addition, an automated approach is developed to extract and summarize gender requirements from unstructured text in accordance with the gender model. This approach consists of: 1) the feature extraction module, and 2) the feature summarization module. The first module identifies and extracts gender features using heuristic rules and automatically-generated patterns. The second module summarizes gender requirements by relation inference. Results Based on 100,134 clinical trials from ClinicalTrials.gov, our approach was compared with 20 commonly applied machine learning methods. It achieved a macro-averaged precision of 0.885, a macro-averaged recall of 0.871 and a macro-averaged F1-measure of 0.878. The results illustrated that our approach outperformed all baseline methods in terms of both commonly used metrics and macro-averaged metrics. Conclusions This study presented a new gender model aiming for specifying the transgender requirement more precisely. We also proposed an approach for gender information extraction and summarization from unstructured clinical text to enhance transgender-related clinical trial population recruitment. The experiment results demonstrated that the approach was effective in transgender criteria extraction and summarization.

Abstract Background Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field. Methods With the basis of 4361 publications from both Web of Science and PubMed during the year 2008–2017, the analysis utilizes methods including topic modelling and science mapping analysis. Results Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected. Conclusions This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction.

Background: The pharmacokinetics and appropriate dose regimens of favipiravir are unknown in hospitalized influenza patients; such data are also needed to determine dosage selection for favipiravir trials in COVID-19. Methods: In this dose-escalating study, favipiravir pharmacokinetics and tolerability were assessed in critically ill influenza patients. Participants received one of two dosing regimens; Japan licensed dose (1600 mg BID on day 1 and 600 mg BID on the following days) and the higher dose (1800 mg/800 mg BID) trialed in uncomplicated influenza. The primary pharmacokinetic endpoint was the proportion of patients with a minimum observed plasma trough concentration (Ctrough) ≥20 mg/L at all measured time points after the second dose. Results: Sixteen patients were enrolled into the low dose group and 19 patients into the high dose group of the study. Favipiravir Ctrough decreased significantly over time in both groups (p 80% of the duration of treatment of the two dose regimens evaluated (18.8% and 42.1% of patients for low and high dose regimen, respectively). Increasing the favipravir dosage predicted a higher proportion of patients reaching this threshold of 20 mg/L, suggesting that dosing regimens of ≥3600/2600 mg might be required for adequate concentrations. The two dosing regimens were well-tolerated in critical ill patients with influenza. Conclusion: The two dosing regimens proposed for uncomplicated influenza did not achieve our pre-defined treatment threshold.

Endometriosis is a common benign disease in women of reproductive age. It has been defined as a disorder characterized by inflammation, compromised immunity, hormone dependence, and neuroangiogenesis. Unfortunately, the mechanisms of endometriosis have not yet been fully elucidated, and available treatment methods are currently limited. The discovery of new therapeutic drugs and improvements in existing treatment schemes remain the focus of research initiatives. Chinese medicine can improve the symptoms associated with endometriosis. Many Chinese herbal medicines could exert antiendometriosis effects via comprehensive interactions with multiple targets. However, these interactions have not been defined. This study used association rule mining and systems pharmacology to discover a method by which potential antiendometriosis herbs can be investigated. We analyzed various combinations and mechanisms of action of medicinal herbs to establish molecular networks showing interactions with multiple targets. The results showed that endometriosis treatment in Chinese medicine is mainly based on methods of supplementation with blood-activating herbs and strengthening qi. Furthermore, we used network pharmacology to analyze the main herbs that facilitate the decoding of multiscale mechanisms of the herbal compounds. We found that Chinese medicine could affect the development of endometriosis by regulating inflammation, immunity, angiogenesis, and other clusters of processes identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The antiendometriosis effect of Chinese medicine occurs mainly through nervous system–associated pathways, such as the serotonergic synapse, the neurotrophin signaling pathway, and dopaminergic synapse, among others, to reduce pain. Chinese medicine could also regulate VEGF signaling, toll-like reporter signaling, NF-κB signaling, MAPK signaling, PI3K-Akt signaling, and the HIF-1 signaling pathway, among others. Synergies often exist in herb pairs and herbal prescriptions. In conclusion, we identified some important targets, target pairs, and regulatory networks, using bioinformatics and data mining. The combination of data mining and network pharmacology may offer an efficient method for drug discovery and development from herbal medicines.

Abstract Background Temporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts. Methods A novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple , TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N. Results Based on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries. Conclusions An automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.

Abstract Background Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Results Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Conclusions Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient’s risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies.

Abstract After publication of the original article [1] it was noted that the captions relating to Figs. 2 and 3 had been interchanged.