BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

Objective:To investigate the biomechanical correlation between ankle instability and osteochondral lesions of the talus (OLT) under loading conditionsMethods:A healthy 29-year-old male volunteer was selected for the study. A 64-slice spiral CT scan of the right lower limb was performed to construct a detailed finite element model of the ankle joint, including ligaments and cartilage. Three injury models were created: models of distal tibiofibular syndesmosis injury, lateral collateral ligament injury, and a combined injury of the distal tibiofibular syndesmosis and lateral collateral ligament. Differences in stress distribution on the tibiotalar joint surface, talus stress, and talus displacement were analyzed through anterior drawer test, inversion stress test, and external rotation stress test.Results:In the anterior drawer test, as the forward traction force increased (40, 60, 80, 100, 120, 140, and 150 N), all the injury models showed a progressive increase in tibiotalar joint surface stress, talus stress, and talus displacement. The combined injury model showed the highest tibiotalar joint surface stress (32.6 MPa), while the lateral collateral ligament injury model demonstrated the highest talus stress (56.5 MPa). Talus displacement increased significantly with traction, reaching the maximum (4.88 mm) in the combined injury model under 150 N. In the inversion stress test, stress on the tibiotalar joint surface in the lateral collateral ligament injury model was concentrated on the posterior-lateral and posterior-medial regions, whereas in the combined injury model, stress on the tibiotalar joint surface was predominantly concentrated in the posterior-medial region. Talus stress was localized to the talus neck and body in all the models, with the combined injury model showing the largest talus displacement (8.46 mm). In the external rotation stress test, stress on the tibiotalar joint surface was mainly distributed in the posterior-medial, posterior-lateral, and anterior-lateral regions in all the models. Talus stress was concentrated at the talus neck and body. The combined injury model exhibited the greatest talus displacement (12.50 mm).Conclusion:Ankle instability, particularly from combined injuries of the distal tibiofibular syndesmosis and lateral collateral ligament, significantly increases the stress concentration and talus displacement under loading conditions, thus elevating the risk of OLT.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Objective:To investigate the biomechanical correlation between ankle instability and osteochondral lesions of the talus (OLT) under loading conditionsMethods:A healthy 29-year-old male volunteer was selected for the study. A 64-slice spiral CT scan of the right lower limb was performed to construct a detailed finite element model of the ankle joint, including ligaments and cartilage. Three injury models were created: models of distal tibiofibular syndesmosis injury, lateral collateral ligament injury, and a combined injury of the distal tibiofibular syndesmosis and lateral collateral ligament. Differences in stress distribution on the tibiotalar joint surface, talus stress, and talus displacement were analyzed through anterior drawer test, inversion stress test, and external rotation stress test.Results:In the anterior drawer test, as the forward traction force increased (40, 60, 80, 100, 120, 140, and 150 N), all the injury models showed a progressive increase in tibiotalar joint surface stress, talus stress, and talus displacement. The combined injury model showed the highest tibiotalar joint surface stress (32.6 MPa), while the lateral collateral ligament injury model demonstrated the highest talus stress (56.5 MPa). Talus displacement increased significantly with traction, reaching the maximum (4.88 mm) in the combined injury model under 150 N. In the inversion stress test, stress on the tibiotalar joint surface in the lateral collateral ligament injury model was concentrated on the posterior-lateral and posterior-medial regions, whereas in the combined injury model, stress on the tibiotalar joint surface was predominantly concentrated in the posterior-medial region. Talus stress was localized to the talus neck and body in all the models, with the combined injury model showing the largest talus displacement (8.46 mm). In the external rotation stress test, stress on the tibiotalar joint surface was mainly distributed in the posterior-medial, posterior-lateral, and anterior-lateral regions in all the models. Talus stress was concentrated at the talus neck and body. The combined injury model exhibited the greatest talus displacement (12.50 mm).Conclusion:Ankle instability, particularly from combined injuries of the distal tibiofibular syndesmosis and lateral collateral ligament, significantly increases the stress concentration and talus displacement under loading conditions, thus elevating the risk of OLT.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
Machine Learning ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional/methods* ; Humans ; Quality Control

Identification of critical material attributes(CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets. This study focuses on the granules of Jianwei Xiaoshi Tablets, using tablet tensile strength as the primary quality attribute. A method for identifying the CMAs and a design space for the granules were established, along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy(FT-NIR). First, granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments(DOE). The powder properties of the granules were measured. An orthogonal partial least squares(OPLS) model was established to correlate the powder properties with tensile strength. Based on the characteristics of the comprehensive variables extracted by OPLS, the independent variables with the greatest explanatory power for tensile strength were identified. FT-NIR technology was then employed to establish a predictive model for the granule CMAs. The final CMAs identified were hygroscopicity, moisture content, D_(50), collapse angle, mass flow rate, and tapped density. The coefficients of determination of the prediction set(R■) and relative percentage deviation(RPD) of the prediction set for flowability, D_(50), and moisture content were 0.891, 0.994, and 0.998; and 2.97, 12.4, and 20.7, respectively. The established OPLS model clearly identified the impact of various factors on tensile strength, demonstrating good fit results. The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
Drugs, Chinese Herbal/chemistry* ; Tablets/chemistry* ; Tensile Strength ; Quality Control ; Spectroscopy, Fourier Transform Infrared ; Spectroscopy, Near-Infrared

Immunoassays are widely used in medicine, food, environment and other fields due to having the advantages of simpleness, rapidness and accuracy. Combining immunoassays with nanomaterials can improve the performance of immunoassays. Compared with traditional nanomaterials, upconversion nanoparticles (UCNPs) have excellent optical properties such as good photostability, long luminescence lifetime and narrow and tunable emission bands, which can significantly reduce background noise and improve analytical sensitivity when combined with immunoassay. This paper briefly introduces the luminescence mechanism of UCNPs, summarizes the synthesis and surface modification methods of UCNPs. And then 5 UCNPs-based immunoassay techniques, namely, fluorescence resonance energy transfer, inner filter effect, magnetic separation technique, upconversion-linked immunosorbent assay and upconversion immunochromatography, are discussed in detail. These sensing protocols of UCNPs-based immunoassays have been successfully utilized to detect various targets, including small molecules, macromolecules, and pathogens, all of which closely related to food safety, human health, and environmental pollution. Finally, the challenges and prospects of this technique are summarized and prospected. Although the UCNPs immunoassays based on antibodies and antigens have made great progress, most of the research is still in the stage of laboratory, and there is a long way to go to realize its social applications. There is a series of challenges need to be overcome. (1) Designing excellent water soluble and dispersive upconversion nanomaterials is needed. Hydrophilic ligands are bound to smaller upconversion nanoparticles and removing hydrophobic surface ligands are the most widely used methods to improve solubility and dispersity. (2) Multi-detection technology platforms and multi-mode simultaneous detection platforms have great potential, which will improve the efficiency of point of care detection. (3) The researchers also need to focus on some important problems. For examples, the upconversion luminescence efficiency of UCNPs is difficult to maintain, the synthesis method is complex, and the surface modification degree and functionalization are difficult to control.

Objective To investigate the effects of epithelial transformation sequence 2(ECT2)and p33ING1 on the metastatic activity of esophageal squamous cell carcinoma(ESCC)cells.Methods The expressions of ECT2 and p33ING1 in esophageal squamous cell carcinoma tissues and adjacent tissues were detected by immunohistochemistry and Western blotting.Human esophageal squamous carcinoma cell line KYSE140 cells were divided into 4 groups:blank group,negative control(pcDNA 3.1 NC)group,overexpression group(pcDNA 3.1 ECT2)and inhibited expression group(si ECT2).MTT assay and cell colony formation assay were used to study the proliferation and growth ability of cells,Transwell assay and scratch assay used to study the invasion and migration ability of cells,and flow cytometry used to detect apoptosis and cell cycle,Western blotting used to detect the effect of ECT2 on p33ING1 protein.Results ECT2 expression increased and p33ING1 expression decreased in esophageal squamous cell carcinoma tissues.Overexpression of ECT2 significantly increased the growth,colony formation,migration and invasion abilities of KYSE140 cells,and decreased the apoptosis rate and p33ING1 expression of KYSE140 cells.In addition,inhibition of ECT2 expression could reverse the above changes.Conclusion The high expression of ECT2 can promote the growth and metastasis of esophageal squamous cell carcinoma KYSE140 cells and inhibit their apoptosis.The mechanism may be related to the inhibition of p33ING1 expression by ECT2.