Objective To explore the surgical treatment strategy for patients with thyroid cancer complicated with primary hyperparathyroidism(PHPT).Methods A retrospective analysis was conducted on the case data of thyroid cancer patients who underwent surgical treatment at Beijing Friendship Hospital,Capital Medical University from January 2016 to January 2022.Among them,22 patients with PHPT were included in study group,and 44 patients were randomly selected from thyroid cancer patients during the same period at a ratio of 1∶2 and included in control group.The operation time,intraoperative blood loss,hospital stay and occurrence of complications of two groups of patients were compared.Results The operation time of patients in study group was significantly longer than that in control group(P＜0.01).There was no statistically significant difference in intraoperative blood loss and hospital stay between two groups of patients(P＞0.05).There were 5 cases of temporary hypocalcemia in study group,12 cases of temporary hypocalcemia and 1 case of incision infection in control group.There was no statistically significant difference in incidence of postoperative complications between two groups of patients(P＞0.05).Conclusion Thyroid cancer complicated with PHPT increases the complexity of treatment.Through adequate preoperative diagnosis and reasonable surgical treatment strategies,the risks of secondary surgery and postoperative complications can be effectively reduced,and the prognosis of patients can be improved.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

ObjectiveTo investigate the intervention effects of Suanzaoren Tang on depression model rats induced by isolation combined with chronic unpredictable mild stress （CUMS）， and to examine its influence on the c-Jun N-terminal kinase （JNK）/proto-oncogene protein （c-Myc）/tumor suppressor protein 53 （p53） signaling pathway， thereby revealing its potential functional mechanism. MethodsA total of 72 male SD rats were randomly divided into six groups using a strict random number table： blank group， model group， fluoxetine group （3.6 mg·kg^-1）， and high-， medium-， and low-dose Suanzaoren Tang groups （10， 5， 2.5 g·kg^-1），with 12 rats in each group. A depression model was established using isolation combined with CUMS. Fluoxetine and different doses of Suanzaoren Tang were administered continuously for 28 days. Behavioral indicators such as sucrose water consumption and open field test scores were recorded. Western blot and immunohistochemistry （IHC） were employed to analyze the expression of key proteins in the JNK/c-Myc/p53 signaling pathway， and the terminal deoxynucleotidyl transferase dUTP nick end labeling （TUNEL） assay was used to evaluate the number of apoptotic cells in the hippocampus. ResultsCompared with the blank group， the model group exhibited a significantly reduced sucrose preference index （P<0.01）， a lower total score of horizontal and vertical movements in the open field test （P<0.01）， significantly increased expression of JNK， c-Myc， and p53 proteins in the hippocampus （P<0.01）， and a higher number of TUNEL-positive cells in the hippocampus （P<0.01）. Compared with the model group， the sucrose preference index and the total score of horizontal and vertical movements in the open field test significantly increased in the high- and medium-dose Suanzaoren Tang groups and the fluoxetine group （P<0.05， P<0.01）. The expression of JNK， c-Myc， and p53 proteins significantly decreased in all Suanzaoren Tang groups （high， medium， and low doses） and the fluoxetine group （P<0.05， P<0.01）. The number of TUNEL-positive cells in the hippocampus also significantly decreased in these groups （P<0.01）. ConclusionSuanzaoren Tang can regulate the expression of JNK/c-Myc/p53 proteins in the hippocampus of depression model rats， and its antidepressant mechanism may be related to its protective effect on hippocampal neurons.

With the aging of the global population， osteoporosis （OP） is becoming a major public health concern worldwide. Currently， the commonly used anti-osteoporosis drugs in clinical practice have limited application due to many side effects. Therefore， developing more effective and safer strategies for the prevention and treatment of OP has become a research focus in this field. In recent years， the clinical efficacy and advantages of traditional Chinese medicine （TCM） in treating OP have been gradually recognized. With the deepening pharmacological research on TCM for OP prevention and treatment， it is found that the active ingredients of Scutellaria baicalensis can promote bone formation or inhibit bone resorption by regulating signaling pathways， including Wnt/β-catenin， osteoprotegerin （OB）/receptor activator of nuclear factor-κB ligand （RANKL）/RANK （OPG/RANKL/RANK）， and bone morphogenetic protein 2 （BMP-2）/Smad， mitogen-activated protein kinase （MAPK）， and mammalian target of rapamycin （mTOR）. However， existing research on active ingredients of S. baicalensis for OP treatment is scattered， making it difficult for scholars to gain a systematic understanding of its research and application. This review summarized the literature on the active ingredients of S. baicalensis in OP treatment worldwide， clarified their mechanisms of action， and explored some issues， providing references for the integration of TCM in OP prevention and treatment.

With the aging of the global population， osteoporosis （OP） is becoming a major public health concern worldwide. Currently， the commonly used anti-osteoporosis drugs in clinical practice have limited application due to many side effects. Therefore， developing more effective and safer strategies for the prevention and treatment of OP has become a research focus in this field. In recent years， the clinical efficacy and advantages of traditional Chinese medicine （TCM） in treating OP have been gradually recognized. With the deepening pharmacological research on TCM for OP prevention and treatment， it is found that the active ingredients of Scutellaria baicalensis can promote bone formation or inhibit bone resorption by regulating signaling pathways， including Wnt/β-catenin， osteoprotegerin （OB）/receptor activator of nuclear factor-κB ligand （RANKL）/RANK （OPG/RANKL/RANK）， and bone morphogenetic protein 2 （BMP-2）/Smad， mitogen-activated protein kinase （MAPK）， and mammalian target of rapamycin （mTOR）. However， existing research on active ingredients of S. baicalensis for OP treatment is scattered， making it difficult for scholars to gain a systematic understanding of its research and application. This review summarized the literature on the active ingredients of S. baicalensis in OP treatment worldwide， clarified their mechanisms of action， and explored some issues， providing references for the integration of TCM in OP prevention and treatment.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective To explore the surgical treatment strategy for patients with thyroid cancer complicated with primary hyperparathyroidism(PHPT).Methods A retrospective analysis was conducted on the case data of thyroid cancer patients who underwent surgical treatment at Beijing Friendship Hospital,Capital Medical University from January 2016 to January 2022.Among them,22 patients with PHPT were included in study group,and 44 patients were randomly selected from thyroid cancer patients during the same period at a ratio of 1∶2 and included in control group.The operation time,intraoperative blood loss,hospital stay and occurrence of complications of two groups of patients were compared.Results The operation time of patients in study group was significantly longer than that in control group(P＜0.01).There was no statistically significant difference in intraoperative blood loss and hospital stay between two groups of patients(P＞0.05).There were 5 cases of temporary hypocalcemia in study group,12 cases of temporary hypocalcemia and 1 case of incision infection in control group.There was no statistically significant difference in incidence of postoperative complications between two groups of patients(P＞0.05).Conclusion Thyroid cancer complicated with PHPT increases the complexity of treatment.Through adequate preoperative diagnosis and reasonable surgical treatment strategies,the risks of secondary surgery and postoperative complications can be effectively reduced,and the prognosis of patients can be improved.

Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.