Background: The World Health Organization published the 2019 Integrated Care for Older People (ICOPE) framework to guide, assess, and promote the intrinsic capacity (IC) of older adults, referring to their physical and mental health. This study aims to investigate the relationship between IC and frailty among older adults. Methods: This cross-sectional study was conducted in a medical center in Taiwan in 2021. Two hundred ten patients over 65 admitted to the geriatric ward were invited to participate. The questionnaire included an IC measure, Fried Frailty Scale, and demographic items. The IC measure was ascertained using the six domains of ICOPE (cognition, mobility, nutrition, visual, hearing, and depressive symptoms). The Fried Frailty Scale was used to categorize participants as robust (Fried Frailty Scale=0), prefrail (Fried Frailty Scale=1-2), or frail (Fried Frailty Scale ≥3). Multinomial logistic regression was used to analyze the association between individual ICOPE domains and frailty stages, while adjusting for confounders. Results: Among the participants, 39.0% were prefrail, and 28.6% were frail. Limited mobility and depressive symptoms were significantly associated with prefrail (adjusted odds ratio [aOR]=4.44, 95% confidence interval [CI] 1.82–10.82; aOR=8.41, 95% CI 1.75–40.37) and frail (aOR=11.57, 95% CI 3.63–36.93; aOR=13.77, 95% CI 2.62–72.49) individuals, respectively. Malnutrition (aOR=4.01, 95% CI 1.18–13.62) and hearing loss (aOR=4.37, 95% CI 1.09–19.66) were significantly associated with frail older adults. Conclusion Limited mobility and depressive symptoms occurring at the prefrail stage could be used as assessment items for early detection of prefrail.

Objective This study aimed to investigate the seasonal variation of mosquitoes in Yantai, Shandong Province, and provide a scientific basis for mosquito and mosquito-borne disease control. Methods From March to November for each of the years between 2020 and 2023, adult mosquitoes were collected from different habitats, including parks, hospitals, urban residential areas, rural households and livestock sheds, in 13 counties of Yantai City using a mosquito trap lamp method, among which, female mosquitoes were separated and identified, and the numbers were counted. We used the circular distribution method to analyze the temporal distribution characteristics of mosquito density. Results The γ values obtained for mosquitoes in Yantai from 2020 to 2023, the distribution of mosquitoes in different habitats, and dominant mosquito species in different months showed strong seasonality, with values ranging from 0.7461 to 0.8531(P ＜ 0.05). The peak period of seasonal variation in mosquitoes occurred from June 28thto September 13th, with August 5thbeing identified as the peak day. The peak periods of seasonal variation in mosquitoes in residential areas, parks, hospitals, peasant households, and animals sheds were observed from June 24thto September 14th, June 25thto September 10th, June 22ndto September 12th, July 1stto September 9th, and July 2ndto September 17th, respectively, with corresponding peak days on August 4th,3rd, 2nd, 5th, and 9th, respectively. The peak periods of Culex pipiens pallens, Aedes albopictus, and Anopheles sinensis were from June 26thto September 11th, June 28thto September 17th, and July 12thto September 17th, respectively, with corresponding peak days on August 3rd,7th, and 14th, respectively. Conclusions Our findings in this study revealed that the peak of mosquito activity in Yantai was concentrated in early August, with a peak period extending from June 28thto September 13th, and there were differences between different habitats(with peak periods for rural households and livestock sheds occurring later than those in residential areas, parks, and hospitals). Moreover, with respect to the mosquito species, populations of Cx. pipiens pallens and Ae. albopictus peaked earlier than those of An. sinensis. Based on these findings, we recommend that city-wide mosquito control work should commence in late June, and include appropriate early prevention and control measures for key habitats, such as livestock sheds, and dominant mosquito species, such as Cx. pipiens pallens, to minimize the density of mosquito vectors and the risk of disease transmission.

Introduction: The global rise in paediatric obesity has led to an increasing burden of metabolic comorbidities, including metabolic dysfunction–associated steatotic liver disease (MASLD). Obstructive sleep apnea (OSA), characterized by chronic intermittent hypoxia, has been implicated in the pathogenesis of liver injury. This study aimed to evaluate the prevalence of MASLD and its association with OSA among children and adolescents in a tertiary centre. Methodology: A retrospective cross-sectional study was conducted involving children aged 6–15 years who were admitted for polysomnography at Sarawak General Hospital between 2024 and 2025. Clinical, biochemical, and lifestyle data were obtained from medical records and parental questionnaires. Hepatic steatosis was assessed using hepatobiliary ultrasound in a subset of patients. Results: A total of 132 children (mean age 10.8 ± 2.7 years; 87.5% male) were included. The median BMI SDS was 2.50 (IQR: 2.29– 2.93). The mean apnea–hypopnea index (AHI) was 12.0 ± 7.9, with 60% classified as severe OSA. Among the 16 children who underwent liver ultrasound, MASLD was identified in 81.2%, including five cases of grade 1 and eight cases of grade 2 steatosis. Liver transaminitis was present in six patients. Metabolic comorbidities were common, with 43.8% requiring metformin for insulin resistance or impaired glucose tolerance, and four patients requiring antihypertensive therapy. Unhealthy lifestyle behaviors were prevalent: 64.2% consumed sugary drinks ≥3 times weekly, 42.9% consumed fast food ≥3 times weekly, and only 24.3% reported daily fruit or vegetable intake. Physical inactivity (≤2 times/week) was observed in 71.4%, while 64.3% had screen time exceeding 2 hours daily. Conclusion Children and adolescents with moderate-to-severe OSA exhibit a high burden of metabolic dysfunction, including MASLD. The findings support a potential contributory role of OSA-related hypoxia in paediatric fatty liver disease and highlight the need for integrated screening and early intervention in this high-risk population.
Adolescent ; Child ; Malaysia ; Liver Diseases

ObjectiveTo investigate the mechanism by which Luoshi Neiyi prescription treats endometriosis （EMs） through the Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB） signaling pathway. MethodsAnimal experiments were conducted with 50 female SD rats， which were randomized into a sham operation group （10 rats） and a modeling group （40 rats）. An autologous endometrial transplantation method was used for the modeling of EMs. The 36 successfully modeled rats were randomly allocated into four groups （n=9 each）： The model group， the low-dose （7.87 g·kg^-1） Luoshi Neiyi prescription group， the high-dose （15.74 g·kg^-1） Luoshi Neiyi prescription group， and the dienogest （0.20 mg·kg^-1） group. The physiological status and ectopic lesion volumes of rats in each group were observed. Hematoxylin-eosin （HE） staining was used to observe the morphology of the eutopic endometrial tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the levels of inflammatory factors including interleukin-1β （IL-1β） and prostaglandin E₂ （PGE₂） in the serum of EMs rats. Immunohistochemistry was used to detect the expression of matrix metalloproteinase-9 （MMP-9） and vascular endothelial growth factor A （VEGFA） in the eutopic endometrial tissue. Western blot was adopted to determine the protein levels of TLR4， myeloid differentiation factor 88 （MyD88）， phosphorylated nuclear factor-κB （p-NF-κB）/NF-κB， MMP-9， and VEGFA in the eutopic endometrial tissue. In the cell experiments， the cell-counting kit-8 （CCK-8） assay was employed to screen the optimal concentration of Luoshi Neiyi prescription-containing serum， and a scratch assay was performed to assess the migration ability of iheESCs cells. Interventions with Luoshi Neiyi prescription-containing serum， resatorvid （TAK-242， a TLR4 inhibitor）， and lipopolysaccharides （LPS， a TLR4 agonist） were conducted， and Western blot was used to detect the expression of proteins related to the TLR4/NF-κB signaling pathway. ResultsThis experiment successfully replicated 36 EMs rat models. Compared with the sham operation group， the model group exhibited visible ectopic lesions on the abdominal wall and an increase in the writhing response score （P<0.01）. Furthermore， HE staining revealed the model group exhibited a thickened eutopic epithelium， stromal cell disarrangement， and evident infiltration of inflammatory cells. In addition， the model group showed elevated serum levels of IL-1β and PGE₂ （P<0.05， P<0.01）， increased positive expression of MMP-9 and VEGFA in the ectopic endometrial tissue （P<0.01）， and up-regulated protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01）. Compared with the model group， all treatment groups exhibited a reduction in the ectopic lesion volume （P<0.01）. Furthermore， the writhing response scores were decreased in the high-dose Luoshi Neiyi prescription group and the dienogest group （P<0.05， P<0.01）. The pathological state of the ectopic endometrial tissue was alleviated to varying degrees in the treatment groups. Low-dose Luoshi Neiyi prescription reduced serum PGE₂ levels， and high-dose Luoshi Neiyi prescription and dienogest decreased serum IL-1β and PGE₂ levels in EMs rats （P<0.05， P<0.01）. The treatment groups showed decreased positive expression of MMP-9 and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01） and down-regulated protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01）. The cell experiments showed that 5%， 10%， and 20% Luoshi Neiyi prescription-containing sera significantly reduced the viability and inhibited the migration of iheESCs. Compared with the control group， 5%， 10%， and 20% Luoshi Neiyi prescription-containing serum groups and the TAK-242 group showed reduced protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA （P<0.01）. Compared with the control group， the LPS group showed increased expression of the above proteins （P<0.01）. Compared with the LPS group， the LPS+5%， 10%， and 20% Luoshi Neiyi prescription-containing serum groups showed reduced expression of the above proteins （P<0.05， P<0.01）. ConclusionLuoshi Neiyi prescription may modulate the TLR4/NF-κB signaling pathway to reduce the inflammatory response and histopathological damage in the eutopic endometrium and suppress the adhesive， invasive， and angiogenic biological processes in the ectopic endometrial tissue， thereby exerting its therapeutic effect on EMs.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

BACKGROUND:Allogeneic hematopoietic stem cell transplantation is an important treatment for malignant hematological diseases,and delayed postoperative platelet implantation is a common complication that seriously affects the quality of patient survival;however,there are no standard protocols to improve platelet implantation rates and prevent platelet implantation delays. OBJECTIVE:To compare the safety and efficacy of oral Herombopag Olamine versus subcutaneous recombinant human thrombopoietin for promoting platelet implantation in patients with malignant hematological diseases undergoing haploid hematopoietic stem cell transplantation. METHODS:Clinical data of 163 patients with malignant hematological diseases who underwent haploidentical hematopoietic stem cell transplantation from January 2016 to October 2022 were retrospectively analyzed.A total of 72 patients who started to subcutaneously inject recombinant human thrombopoietin at+2 days were categorized into the recombinant human thrombopoietin group;a total of 27 patients who started to orally take Herombopag Olamine at+2 days were categorized into the Herombopag Olamine group;and 64 patients who did not apply Herombopag Olamine or recombinant human thrombopoietin were categorized into the blank control group.The implantation status,incidence of acute graft-versus-host disease of degree II-IV within 100 days,1-year survival rate,1-year recurrence rate,and safety were analyzed in the three groups. RESULTS AND CONCLUSION:(1)The average follow-up time was 52(12-87)months.The implantation time of neutrophils in the blank control group,recombinant human thrombopoietin group,and Herombopag Olamine group was(12.95±3.88)days,(14.04±3.71)days,and(13.89±2.74)days,respectively,with no statistically significant difference(P=0.352);the implantation time of platelets was(15.16±6.27)days,(17.67±6.52)days,and(17.00±4.75)days,with no statistically significant difference(P=0.287).(2)The complete platelet implantation rate on day 60 was 64.06%,90.28%,and 92.59%,respectively,and the difference was statistically significant(P<0.001).The subgroup analysis showed that the difference between the blank control group and the recombinant human thrombopoietin group was statistically significant(P<0.001),and the difference between the blank control group and the Herombopag Olamine group was statistically significant(P=0.004).The difference was not statistically significant between the recombinant human thrombopoietin group and Herombopag Olamine group(P=0.535).(3)100-day II-IV degree acute graft-versus-host disease incidence in the blank control group,recombinant human thrombopoietin group,and Herombopag Olamine group were 25.00%,30.56%,and 25.93%,respectively,and the difference was not statistically significant(P=0.752).(4)The incidence of cytomegalovirus anemia,cytomegalovirus pneumonia,and hepatic function injury had no statistical difference among the three groups(P>0.05).(5)During the follow-up period,there was no thrombotic event in any of the three groups of patients.(6)The results showed that recombinant human thrombopoietin and Herombopag Olamine could improve the platelet implantation rate of malignant hematological disease patients after haploidentical hematopoietic stem cell transplantation,with comparable efficacy and good safety.

Background: Hearing loss (HL) is one of the most common sensory disorders, affecting over 5-8% of the world's population. Approximately half of HL cases are attributed to genetic factors. In hereditary deafness, about 75-80% is inherited through autosomal recessive inheritance, and common pathogenic genes include GJB2 and SLC26A4. Pathogenic variants in the SLC26A4gene are the leading cause of hereditary hearing loss in humans, second only to the GJB2 gene. Variants in the SLC26A4gene cause hearing loss, which can be non-syndromic autosomal recessive deafness (DFNB4, OMIM #600791) associated with enlarged vestibular aqueduct (EVA) or Pendred syndrome (Pendred, OMIM #605646). DFNB4 is characterized by sensorineural hearing loss combined with EVA or less common cochlear malformation defect. Pendred syndrome is characterized by bilateral sensorineural hearing loss with EVA and an iodine defect that can lead to thyroid goiter. Currently, it is known that EVA is associated with variants in the SLC26A4 gene and is a penetrant feature of SLC26A4-related HL. Predominant mutations in these genes differ significantly across populations. For instance, predominant SLC26A4 mutations differ among populations, including p.T416P and c.1001G>A in Caucasians, p.H723R in Japanese and Koreans, and c.919-2A>G in Han Taiwanese and Han Chinese. On the other hand, there has been no study of hearing loss related to SLC26A4 gene variants among Mongolians, which is the basis of our research. Aim: We aimed to identify the characteristics of the SLC26A4 gene variants in Mongolian people with Enlarged vestibular aqueduct and Mondini malformation. Materials and Methods: In 2022-2024, We included 13 people with hearing loss and enlarged vestibular aqueduct, incomplete cochlea (1.5 turns of the cochlea with cystic apex- incomplete partition type II- Mondini malformation) were examined by CT scan of the temporal bone in our study. WES (Whole exome sequencing) analysis was performed in the Genetics genetic-laboratory of the National Taiwan University Hospital. Results: Genetic analysis revealed 26 confirmed pathogenic variants of bi-allelic SLC26A4 gene of 8 different types in 13 cases, and c.919-2A>G variant was dominant with 46% (12/26) in allele frequency, and c.2027T>A (p.L676Q) variant 19% (5/26), c.1318A>T(p.K440X) variant 11% (3/26), c.1229C>T (p.T410M) variant 8% (2/26) ) , c.716T>A (p.V239D), c.281C>T (p.T94I), c.1546dupC, and c.1975G>C (p.V659L) variants were each 4% (1/26)- revealed. Two male children, 11 years old (SLC26A4: c.919-2A>G) and 7 years old (SLC26A4: c.919-2A>G:, SLC26A4: c.2027T>A (p.L676Q))had history of born normal hearing and progressive hearing loss. Conclusions 1. 26 variants of bi-allelic SLC26A4 gene mutation were detected in Mongolian people with EVA and Mondini malformation, and c.919-2A>G was the most dominant allele variant, and rare variants such as c.1546dupC, c.716T>A (p.V239D) were detected. 2. Our study shows that whole-exome sequencing (WES) can identify gene mutations that are not detected by polymerase chain reaction (PCR) or NGS analysis.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.