Functional dyspepsia （FD） is a prioritized disease category where traditional Chinese medicine （TCM） demonstrates distinct therapeutic advantages. The current western medicine treatment for FD is mainly based on proton pump inhibitors and prokinetic agents， with digestive enzymes， probiotics and antidepressants serving as adjuvant medication， yet such therapies still have certain limitations. TCM treatment for FD includes oral administration of Chinese herbal formulas and Chinese patent medicines， as well as external TCM therapies such as acupuncture and moxibustion， acupoint application， hot medicinal compress therapy， rubbing with ointment， medicinal iontophoresis， auricular acupoint therapy and tui na （Chinese medical massage）. The combined treatment of FD with integrated TCM and western medicine can significantly improve clinical effectiveness and reduce adverse reactions. The common mechanisms underlying the therapeutic effects of both TCM and western medicine revolve around the core pathological processes of FD， mainly focusing on restoring gastrointestinal motility， regulating the levels of brain-gut peptides， modulating intestinal microecology， and ameliorating inflammatory status. The differential mechanisms lie in the precise targeting feature of western medicine versus the holistic-regulating and multi-target characteristics of TCM， and the two approaches exert a synergistic effect to enhance efficacy. This paper proposes to leverage the advantages of TCM in holistic regulation and the strengths of western medicine in targeted treatment， so as to provide personalized and comprehensive treatment regimens for FD patients.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To evaluate the effect and causality of serum metabolites on the pathogenesis of ulcerative colitis (UC), so as to provide reference for nutritional programs for patients with UC.Methods:Two-sample Mendelian randomization (MR) analysis was performed to estimate the causal relationship between serum metabolites and UC. Genome-wide association studies (GWAS) of 1 400 metabolites were performed, with the metabolites as exposure and UC as outcome. Inverse-variance weighted (IVW) was used to calculate causal estimates. Four other MR methods with different modeling assumptions including MR-Egger, weighted median, weighted mode, and simple mode were used as additional analyses to improve the stability of the results. The results were validated through heterogeneity and pleiotropy tests. Finally, the possible causal metabolites were analyzed by metabolic pathway analysis.Results:MR analysis revealed that 85 metabolites had a possible causal relationship with UC. Among them, phosphatidylglycerol 1,2-dipalmitoyl-gpc (DPPC) ( P=2.75×10 -6) and isovaleryl carnitine (C5) ( P=1.84×10 -5) were significant risk factors for UC. Metabolic pathway analysis identified 5 metabolic pathways that might be affected by these metabolites (all P<0.05), among which the porphyrin ( P=0.004) and pyrimidine metabolic pathways ( P=0.008) had higher confidence in impacting UC. Conclusions:There are causal relationships between some serum metabolites (in particular 1,2-dipalmitoyl-GPC and isovalerylcarnitine) and the risk of UC. The porphyrin and pyrimidine metabolic pathways may impact the pathogenesis of UC.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To evaluate the effect and causality of serum metabolites on the pathogenesis of ulcerative colitis (UC), so as to provide reference for nutritional programs for patients with UC.Methods:Two-sample Mendelian randomization (MR) analysis was performed to estimate the causal relationship between serum metabolites and UC. Genome-wide association studies (GWAS) of 1 400 metabolites were performed, with the metabolites as exposure and UC as outcome. Inverse-variance weighted (IVW) was used to calculate causal estimates. Four other MR methods with different modeling assumptions including MR-Egger, weighted median, weighted mode, and simple mode were used as additional analyses to improve the stability of the results. The results were validated through heterogeneity and pleiotropy tests. Finally, the possible causal metabolites were analyzed by metabolic pathway analysis.Results:MR analysis revealed that 85 metabolites had a possible causal relationship with UC. Among them, phosphatidylglycerol 1,2-dipalmitoyl-gpc (DPPC) ( P=2.75×10 -6) and isovaleryl carnitine (C5) ( P=1.84×10 -5) were significant risk factors for UC. Metabolic pathway analysis identified 5 metabolic pathways that might be affected by these metabolites (all P<0.05), among which the porphyrin ( P=0.004) and pyrimidine metabolic pathways ( P=0.008) had higher confidence in impacting UC. Conclusions:There are causal relationships between some serum metabolites (in particular 1,2-dipalmitoyl-GPC and isovalerylcarnitine) and the risk of UC. The porphyrin and pyrimidine metabolic pathways may impact the pathogenesis of UC.

Objective : To explore the value of combined indicators of prognostic nutritional index ( PNI) ，nutri- tional risk screening 2002 scale ( NRS2002 ) ，body mass index ( BMI) ，monocyte / lymphocyte ratio ( MLR) ， platelet / lymphocyte ratio ( PLR) ，and neutrophil / lymphocyte ratio ( NLR) in evaluating the severity of pulmonary tuberculosis ( PTB) ． Methods : A total of 175 patients with pulmonary tuberculosis were selected as the study group，and 175 healthy subjects who underwent physical examination during the same period were selected as the control group．According to the range of lung lesions，the patients were divided into mild to moderate group ( ＜3 lung fields，n = 110) and severe group ( ≥3 lung fields，n = 65) ，PNI，NRS2002，BMI and peripheral blood MLR , PLR and NLR levels were compared between the two groups．Spearman rank correlation was used to analyze their correlation with the severity of the disease．A multivariate logistic regression model was established．A nomo- gram was drawn，and the efficiency of the model was evaluated by receiver operating characteristic ( ROC) curve， calibration curve，decision curve． Results: The PNI，BMI，peripheral blood lymphocyte and albumin levels in the study group were lower than those in the control group ( P＜0. 05) ，while the MLR , PLR and NLR levels were sig- nificantly higher than those in the control group ( P＜0. 05) ．The NRS2002，MLR , PLR and NLR levels in the se- vere group were higher than those in the mild and moderate group ( P＜0. 05) ，while the PNI and BMI levels were lower than those in the mild and moderate group ( P＜0. 05) ．NRS2002 and peripheral blood MLR , PLR and NLR levels in patients with pulmonary tuberculosis were positively correlated with the severity of the disease ( r = 0. 250， 0. 509，0. 431 and 0. 488) ．PNI and BMI were negatively correlated with the severity of the disease ( r = －0. 516， - 0. 231) ．Multivariate Logistic regression showed that NRS2002 and NLR were independent risk factors for severe disease，while PNI was a protective factor．The areas under the curve ( AUC) of NRS2002，NLR , PNI，and com- bined detection for evaluating the severity of pulmonary tuberculosis were 0. 692，0. 777，0. 786，and 0. 860，re- spectively，the sensitivity of the combined prediction was 81. 54% and the specificity was 76. 36%．The combined detection had better evaluation efficiency for severe pulmonary tuberculosis than the single indicator ( P ＜0. 05) ． Conclusion NRS2002 and NLR are independent risk factors for severe disease，while PNI is a protective factor． The combined detection model has a good fit，which can improve the evaluation efficiency and has potential for clin- ical application．

Purpose/Significance To investigate the application of cutting-edge technologies in predicting the incidence of hemor-rhagic fever with renal syndrome(HFRS),to compile and integrate various time-series analysis methods,evaluate and select the opti-mal model.Method/Process By utilizing national HFRS incidence data from 2004 to 2020,the effectiveness of models is predicted based on statistical methods:SARIMA,STL-ARIMA and TBATS,neural network approaches:NNAR,LSTM and combined models of SARIMA-LSTM with 3 different weighting schemes.The performance of these models is comprehensively assessed using RMSE,MAE and MAPE.Result/Conclusion The SARIMA and LSTM models are identified as the superior individual models.The combined SARI-MA-LSTM model demonstrates enhanced performance compared to individual models.The SARIMA-LSTM model optimized using the reciprocal of error method is deemed the optimal model.The optimal model is expected to provide technical support and references for the early warning system model design of HFRS.

Pulmonary blast injury has become the main type of trauma in modern warfare, characterized by externally mild injuries but internally severe injuries, rapid disease progression, and a high rate of early death. The injury is complicated in clinical practice, often with multiple and compound injuries. Currently, there is a lack of effective protective materials, accurate injury detection instrument and portable monitoring and transportation equipment, standardized clinical treatment guidelines in various medical centers, and evidence-based guidelines at home and abroad, resulting in a high mortality in clinlcal practice. Therefore, the Trauma Branch of Chinese Medical Association and the Editorial Committee of Chinese Journal of Trauma organized military and civilian experts in related fields such as thoracic surgery and traumatic surgery to jointly develop the Clinical treatment guideline for pulmonary blast injury ( version 2023) by combining evidence for effectiveness and clinical first-line treatment experience. This guideline provided 16 recommended opinions surrounding definition, characteristics, pre-hospital diagnosis and treatment, and in-hospital treatment of pulmonary blast injury, hoping to provide a basis for the clinical treatment in hospitals at different levels.

Objective:To explore the effects of online supportive disclosure therapy on the self-expression level, professional identity, dimission intention, and turnover rate of pre-dimission nurses, in order to provide a reference for nursing managers to stabilize the nursing team.Methods:A quasi experimental research method was used, and a convenient sampling method was used to select 192 pre resigned nurses from Shandong Provincial Third Hospital, the First Affiliated Hospital of Shandong First Medical University, and Shandong First Medical University Affiliated Provincial Hospital from October 2021 to December 2022 as the research subjects. They were divided into a control group and an experimental group by random number table method, with 96 nurses in each group. The control group received routine exit interviews, while the experimental group received online supportive disclosure therapy intervention based on this. The self-expression level, professional identity, dimission intention, and turnover rate of two groups of nurses before and 1, 2 months after the intervention were evaluated.Results:Finally, 94 nurses in the control group and 92 nurses in the experimental group completed the study. There were no significant differences in the self-expression level, professional identity, dimission intention before the intervention between the two groups ( P>0.05). After 1 and 2 months of the intervention, the scores of the Pain Self Disclosure Index, Nurse Professional Identity Rating Scale, and Resignation Intention Scale of the experimental group were 36.33 ± 5.13, 73.88 ± 8.72, 14.18 ± 1.12 and 34.22 ± 6.78, 98.26 ± 11.29, 6.16 ± 1.19, respectively,and the control group were 28.06 ± 8.23, 64.72 ± 10.39, 17.82 ± 1.37 and 44.26 ± 7.62, 79.82 ± 8.66, 9.18 ± 1.06, there were statistically significant differences between the two groups ( t values were -13.54 to -2.11, all P<0.05); there were statistically significant differences in the inter group effects, time effects, and interaction effects of the scores on the Pain Self Disclosure Index, Professional Identity Rating Scale, and Resignation Intention Scale between the two groups ( F values were 5.12 to 14.82, all P<0.05). The turnover rate of nurses in the experimental group was 1.09% (1/92), lower than 8.51% (8/94) in the control group, and the difference between the two groups was statistically significant ( χ2=1.59, P<0.05). Conclusions:Online supportive disclosure therapy can improve the self-expression level and professional identity of pre-dimission nurses, and reduce their willingness to resign and turnover rate.

Objective:To investigate the clinical efficacy of Jinghua Weikang capsule combined with Bifidobacterium for curing patients featured by spleen-stomach damp-heat syndrome and Helicobacter pylori (Hp) infection with low DOB values.Methods:To enroll 130 cases who were admitted to the Digestion Center of Beijing Hospital of Traditional Chinese Medicine, Capital Medical University from March 2019 to March 2020. According to the treaatment protocols, the quadruple therapy group and dual therapy group, each had 65 patients. The quadruple therapy group had two different treatment protocols, 34 cases with Rabeprazole sodium enteric-coated tablet, Bismuth potassium citrate capsule, Amoxicillin capsule, and Clarithromycin, the other one had 31 cases with Rabeprazole sodium enteric-coated tablet, Bismuth potassium citrate capsule, Amoxicillin capsule, and Levofloxacin tablets. The Dual therapy group was treated with Jinghua Weikang capsule combined with Bifidobacterium. As for quadruple therapy group, 14 days was a course of treatment, while28 days was a course of treatment for dual therapy group. The two groups were treated for one course, respectively. The TCM syndromes were scored before and after treatment. After 4-weeks long drug withdrawal, all cases were reexamined via 13C-UBT. The Hp eradication rate, efficacy evaluation and adverse reactions were compared between both groups.Results:The eradication rate was 90.8% (59/65) in quadruple therapy group and 78.5% (51/65) in dual therapy group. There was no statistical difference between two groups ( χ２=3.78, P=0.052). As for quadruple therapy group, the eradication rate was 91.2% (31/34) in Protocol One and 90.3% (28/31) in Protocol Two. There was no statistical difference between two protocols ( χ２=0.01, P=0.906). After treatment, the TCM syndrome score of quadruple therapy group [(7.02±0.89) vs. (6.51±0.85), Z=-3.01], was significantly higher than that of dual therapy group ( P<0.01). The total effective rate was 93.9% (61/65) in dual therapy group and 78.5% (51/65) in quadruple therapy group. There was statistically significantly difference between two groups ( χ２=6.45, P=0.011). The adverse reactions was 24.6% (16/65) in quadruple therapy group and 6.2% (4/65) in dual therapy group. There was statistically significantly difference in two groups ( χ２=8.51, P=0.004). Conclusions:The Jinghua Weikang capsule combined with Bifidobacterium had curative effects on Hp infected patients with low DOB values. It could improve TCM Syndromes with little adverse reactions.