Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Objective:To evaluate the clinical equivalence between a domestic calcipotriol/betamethasone dipropionate ointment and the originator product in the treatment of stable plaque psoriasis.Methods:A multicenter, randomized, double-blind, three-arm, parallel-group, active- and placebo-controlled study was conducted, and 449 patients aged 18 - 65 years with stable plaque psoriasis were enrolled from 25 hospitals (such as the First Affiliated Hospital of China Medical University). Eligible patients had a baseline physician's global assessment (PGA) score of ≥ 3 points, baseline body surface area (BSA) involvement of 5% - 30%, and a target lesion psoriasis area and severity index (TL-PASI) for plaque elevation of ≥ 3 points. Participants were randomly assigned in a 2:2:1 ratio to the test group ( n = 179), reference group ( n = 180), and placebo group ( n = 90), and applied the domestic calcipotriol/betamethasone dipropionate ointment, originator product, and ointment base respectively, once daily in the evening for 4 weeks. Efficacy and safety were assessed at weeks 1, 2, and 4. The primary efficacy endpoints were the treatment success rates and clinical success rates in each group at week 4. The per-protocol set (PPS) was used for the primary efficacy analysis, and the intention-to-treat (ITT) set for supplementary efficacy analysis. Equivalence between the test and reference preparations was tested using the Cochran-Mantel-Haenszel method adjusted for randomization strata. Superiority of the test and reference preparations over the placebo was also tested. Measurement data were compared among the 3 groups using analysis of variance or non-parametric tests, while treatment success rates, clinical success rates, and incidence rates of adverse reactions were compared using the chi-square test. Results:The ITT, PPS, and safety sets included 447, 420, and 448 patients, respectively. In the ITT set, patients were aged 43.6 ± 12.8 years, including 320 (71.6%) males and 127 (28.4%) females, and the disease duration was 11.21 ± 9.05 years; 316 (70.7%) had a PGA score of 3 points and 131 (29.3%) had a PGA score of 4 - 5 points. No significant differences in the baseline characteristics (including age, sex, disease duration and disease severity) were observed among the 3 groups (all P > 0.05). Based on the PPS analysis, the treatment success rates were 57.9% (99/171) in the test group, 50.3% (86/171) in the reference group, and 7.7% (6/78) in the placebo group, and the clinical success rates were 57.9% (99/171), 50.3% (86/171), and 10.3% (8/78), respectively; both the test and reference groups were superior to the placebo group in both treatment and clinical success rates (all P < 0.001) ; the rate differences for treatment success (90% confidence interval [ CI]: -1.3% - 16.4%) and clinical success (90% CI: -1.3% - 16.3%) between the test and reference groups were entirely within the pre-defined equivalence margin (-20% - 20%). Subgroup analyses by baseline PGA scores: for patients with a baseline PGA score of 3 points, the treatment success rates in the test, reference, and placebo groups were 60.8% (73/120), 52.1% (62/119), and 11.1% (6/54), respectively, and the corresponding clinical success rates were 61.7% (74/120), 53.8% (64/119), and 13% (7/54), respectively; the test and reference groups did not differ significantly in treatment or clinical success rates (both P > 0.05), but both showed higher success rates than the placebo group (all P < 0.001) ; the results of statistical comparisons among the 3 groups in patients with a baseline PGA score of 4 - 5 points were consistent with those observed in patients with a baseline PGA score of 3 points. The percentage reductions in PGA and TL-PASI scores from baseline to weeks 1, 2, and 4 showed significant differences among the 3 groups, which were significantly higher in the test and reference groups than in the placebo group (all P < 0.001), but did not differ between the test and reference groups (all P > 0.05). The primary adverse reactions were local skin reactions, such as pruritus, pain, and erythema. The incidence rates of adverse reactions were 8.9% (16/179) in the test group, 7.3% (13/179) in the reference group, and 7.8% (7/90) in the placebo group, with no significant difference among the 3 groups ( P > 0.05) . Conclusions:The domestic calcipotriol/betamethasone dipropionate ointment demonstrated clinical equivalence to the originator product in the treatment of stable plaque psoriasis, and the two agents exhibited comparable efficacy for patients with varying degrees of disease severity, and were comparable in the speed and degree of clinical improvement, with similar favorable safety profiles.

A rapid and accurate method for textile fiber identification was developed for quality control and consumer protection.This method utilized electric soldering iron burning-mesh collision enhanced microtube plasma ionization mass spectrometry(ESIB-MC-μTP-MS)to acquire textile fiber MS data and used a random forest(RF)prediction model to identify fiber composition based on these MS data.The MC-μTP device involved in the method was a homemade low-temperature plasma ionization device constructed using cost-effective and readily available components.The system was applicable for direct analysis of small amount of textile samples without any complex sample pretreatment processes.Characteristic thermal decomposition products of different fibers were generated via soldering iron burning(350℃)in ambient atmosphere,and were subsequently analyzed by a mass spectrometer,with each analysis completed within 5 s.Raw MS data underwent noise reduction,normalization,and global binning steps to form a dataset,and its intrinsic class separability was evaluated using principal component analysis(PCA)combined with k-means clustering.Then,the RF model was trained based on the dimensionality-reduced textile fiber dataset.After grid search optimization,this model demonstrated robust performance with a 0.9762 out-of-bag score,a 0.9683 cross-validation accuracy(5-fold),and a 0.9636 test accuracy,supported by precision,recall,and F1-scores exceeding 0.889 for all fiber classes.The method was applied to analysis of 30 luxury apparel samples from eight brands,among which 20 samples achieved 100%prediction confidence,aligning with labeled compositions.The identification result of two low-confidence samples was further confirmed using attenuated total reflection Fourier transform infrared spectroscopy(ATR-FT-IR).The method has been proven to be simple,portable and with minimal sample requirements for on-site customs inspections,providing a viable tool in the fight against counterfeit products,therefore supporting regulatory enforcement and consumer trust in the textile goods market.

Due to the poor ionization efficiency and the weak mass spectrometry(MS)intensity of weakly polar substances,direct analysis using the traditional electrospray ionization mass spectrometry(ESI-MS)is a big challenge.In this study,a novel rapid on-site detection method of four prohibited sex hormones in cosmetics was proposed using online derivatization strategy coupled with a miniature mass spectrometer.The target substances in the samples were extracted by a custom-made polyaniline/multi-walled carbon nanotube solid-phase microextraction(SPME)probe.The stirring speed was 200 r/min,the extraction temperature was 40℃,and the extraction time was 2 min.A pulled dual-channel θ borosilicate glass capillary emitter was used as the nano-ESI ion source.The SPME probe was inserted into the channel containing methanol in theθborosilicate glass capillary.When the spray voltage was applied,the four sex hormones were desorbed and formed spray microdroplets,which then collided with the hydroxylamine microdroplets generated from the other channel.The microdroplets of reaction product entered into the miniature mass spectrometer for direct analysis.The limits of detection(LOD)and limits of quantification(LOQ)for the four sex hormones were 10-20 ng/mL and 20-50 ng/mL,respectively.The recoveries were from 84.6%to 107.8%with the relative standard deviations(RSD)from 4.1%to 11.6%.Compared to detection without derivatization,the MS signals of the four target substances were increased by 3 to 15 times.This method was simple,rapid,highly efficient and sensitive,and suitable for on-site rapid analysis of weakly polar sex hormones in cosmetics.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

OBJECTIVE: To investigate the clinical characteristics and prognosis of peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). METHODS: Clinical data of 10 patients with PTCL-NOS in Gansu Provincial Hospital from May 2016 to June 2023 were collected. The treatment outcomes were evaluated, and the factors affecting prognosis were analyzed. RESULTS: The median age of onset for the 10 patients was 60.7 (47-75) years, with 7 males and 3 females. Nine cases received chemotherapy, while one case died suddenly after diagnosis, and the median course of chemotherapy was 6.9 (1-13) courses. Assessing the efficacy, 3 patients achieved complete remission (CR) while 7 patients showed progression. Age, sex, lactate dehydrogenase (LDH) level, Ki-67 and the presence of hemophagocytic lymphohistocytosis (HLH) were not statistically correlated with CR rate ( P >0.05). Patients with IPI score 3-5, and Ann Arbor stage III-IV had statistically lower CR rates (both P <0.05). Age, B symptoms, LDH level ,hemoglobin, Ki-67 index and PLR value were not statistically correlated with overall survival (OS) time ( P >0.05). Male, platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, presence of HLH, NLR≥4.05, and LMR <2.81 were statistically correlated with shorter OS (all P <0.05). Among the 10 patients, 3 cases have survived and are still in CR status, while 7 cases have died, with a median survival time of 7.5 (1-85) months. CONCLUSIONS Patients with IPI score 3-5 and Ann Arbor stage III-IV have low CR rate and poor prognosis. The OS of patients who are male, with platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, complication of HLH, NLR≥4.05, and LMR <2.81 is short, and prognosis is poor.
Humans ; Lymphoma, T-Cell, Peripheral/diagnosis* ; Male ; Prognosis ; Middle Aged ; Female ; Aged

OBJECTIVE: To investigate the therapeutic effect of Xiangshao Granules (XSG) on post-stroke depression (PSD) and explore the underlying mechanisms. METHODS: Forty-three C57BL/6J mice were divided into 3 groups: sham (n=15), PSD+vehicle (n=14), and PSD+XSG (n=14) groups according to a random number table. The PSD models were constructed using chronic unpredictable mild stress (CUMS) after middle cerebral artery occlusion (MCAO). The sham group only experienced the same surgical operation, but without MACO and CUMS stimulation. The XSG group received XSG (60 mg/kg per day) by gavage for 4 weeks. The mice in the sham and vehicle groups were given the same volume of 0.9% saline at the same time. The body weight and behavior tests including open field test, sucrose preference test, tail suspension test, and elevated plus-maze test, were used to validate the PSD mouse model. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunofluorescence staining were used to evaluate the anti-inflammatory effects of XSG. The potential molecular mechanisms were explored and verified through network pharmacology analysis, Nissl staining, Western blot, ELISA, and RT-qPCR, respectively. RESULTS: The body weight and behavior tests showed that MCAO combined with CUMS successfully established the PSD models. XSG alleviated neuronal damage, reduced the expressions of pro-apoptotic proteins Caspase-3 and B-cell lymphoma-2 (BCL-2)-associated X (BAX), and increased the expression of anti-apoptotic protein BCL-2 in PSD mice (P<0.05 or P<0.01). XSG inhibited microglial activation and the expressions of pro-inflammatory cytokines including tumor necrosis factor-α, interleukin (IL)-1 β, and IL-6 via the toll-like receptor 4/nuclear factor kappa-B signaling pathway in PSD mice (P<0.05 or P<0.01). Furthermore, XSG decreased the expression of indoleamine 2,3-dioxygenase1 (IDO1) and increased the concentration of 5-hydroxytryptamine in PSD mice (P<0.05 or P<0.01). CONCLUSION XSG could reverse the anxiety/depressionlike behaviors and reduce the neuronal injury in the hippocampus and prefrontal cortex of PSD mice, which may be a potential therapeutic agent for PSD.
Animals ; Indoleamine-Pyrrole 2,3,-Dioxygenase/metabolism* ; Depression/etiology* ; Drugs, Chinese Herbal/therapeutic use* ; Hippocampus/metabolism* ; Male ; Mice, Inbred C57BL ; Prefrontal Cortex/pathology* ; Microglia/metabolism* ; Stroke/drug therapy* ; Disease Models, Animal ; Mice ; Behavior, Animal/drug effects*