Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax.

Objective.The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as 'black boxes' due to their lack of interpretability. This situation presents challenges in identifying reasons for misdiagnoses and improving accuracy, which leads to potential risks of misdiagnosis and delayed treatment. Therefore, how to enhance the interpretability of diagnostic models is crucial for improving patient outcomes and reducing treatment delays. So far, only limited researches exist on deep learning-based prediction of spontaneous pneumothorax, a pulmonary disease that affects lung ventilation and venous return.Approach.This study develops an integrated medical image analysis system using explainable deep learning model for image recognition and visualization to achieve an interpretable automatic diagnosis process.Main results.The system achieves an impressive 95.56% accuracy in pneumothorax classification, which emphasizes the significance of the blood vessel penetration defect in clinical judgment.Significance.This would lead to improve model trustworthiness, reduce uncertainty, and accurate diagnosis of various lung diseases, which results in better medical outcomes for patients and better utilization of medical resources. Future research can focus on implementing new deep learning models to detect and diagnose other lung diseases that can enhance the generalizability of this system.

Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer.

Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.

Photoacoustic method for measuring the elasticity of polydimethylsiloxane at various mixing ratios.

Measuring elasticity without physical contact is challenging, as current methods often require deconstruction of the test sample. This study addresses this challenge by proposing and testing a photoacoustic effect-based method for measuring the elasticity of polydimethylsiloxane (PDMS) at various mixing ratios, which may be applied on the wide range of applications such as biomedical and optical fields. A dual-light laser source of the photoacoustic (PA) system is designed, employing cross-correlation signal processing techniques. The platform systems and a mathematical model for performing PDMS elasticity measurements are constructed. During elasticity detection, photoacoustic signal features, influenced by hardness and shapes, are analyzed using cross-correlation calculations and phase difference detection. Results from phantom tests demonstrate the potential of predicting Young's modulus using the cross-correlation method, aligning with the American Society for Testing and Materials (ASTM) standard samples. However, accuracy may be affected by mixed materials and short tubes. Normalization or calibration of signals is suggested for aligning with Young's coefficient.

Aibika Flower Flavonoid Extract Exhibits Antiulcer Activity in a Murine Model of Ethanol-Induced Acute Gastric Injury.

Aibika (Abelmoschus manihot (L.) Medic) is a garden vegetable whose flower has been shown to have various bioactivities. This study investigated the protective effect of aibika flower flavonoid extract (AFF) on ethanol-induced gastric injury in mice. The experimental results showed that pre-feeding 125 and 250 mg AFF/kg BW for 1 week significantly reduced the gastric injury area in the negative control group from 19.2% to 6.7% and 0.6%, respectively. The results of the pathological sections staining also showed that AFF had a protective ability against alcohol-induced injury of gastric tissue and liver tissue. When the mice were exposed to high concentrations of ethanol, AFF pretreatment significantly upregulated the expression of antioxidant enzymes. The pretreatment also promoted the production of the intracellular antioxidant, reduced glutathione, in both gastric tissue and serum. On the contrary, AFF delayed the lipid peroxidation process, which, in turn, reduced the damage to the gastric mucosa. When acute inflammation was induced by ethanol stimulation, AFF significantly downregulated the proinflammatory cytokines and mediators such as TNF-α, IL-1ß, IL-6, NF-κB, COX-2, and iNOS. Furthermore, AFF pretreatment greatly promoted the production of healing factors, such as matrix metalloproteinase (MMP)-2, MMP-7, and MMP-9, in the gastric tissue. In addition, AFF significantly reduced gastric cell apoptosis induced by ethanol stimulation. These results demonstrate that AFF has a good protective effect on alcohol-induced gastric ulcer and has the potential to be used in gastrointestinal health care.

Radiographic chest wall abnormalities in primary spontaneous pneumothorax identified by artificial intelligence.

Primary spontaneous pneumothorax (PSP) primarily affects slim and tall young males. Exploring the etiological link between chest wall structural characteristics and PSP is crucial for advancing treatment methods. In this case-control study, chest computed tomography (CT) images from patients undergoing thoracic surgery, with or without PSP, were analyzed using Artificial Intelligence. Convolutional Neural Network (CNN) model of EfficientNetB3 and InceptionV3 were used with transfer learning on the Imagenet to compare the images of both groups. A heatmap was created on the chest CT scans to enhance interoperability, and the scale-invariant feature transform (SIFT) was adopted to further compare the image level. A total of 2,312 CT images of 26 non-PSP patients and 1,122 CT images of 26 PSP patients were selected. Chest-wall apex pit (CAP) was found in 25 PSP and three non-PSP patients (p < 0.001). The CNN achieved a testing accuracy of 93.47 % in distinguishing PSP from non-PSP based on chest wall features by identifying the existence of CAP. Heatmap analysis demonstrated CNN's precision in targeting the upper chest wall, accurately identifying CAP without undue influence from similar structures, or inappropriately expanding or minimizing the test area. SIFT results indicated a 10.55 % higher mean similarity within the groups compared to between PSP and non-PSP (p < 0.001). In conclusion, distinctive radiographic chest wall configurations were observed in PSP patients, with CAP potentially serving as an etiological factor linked to PSP. This study accentuates the potential of AI-assisted analysis in refining diagnostic approaches and treatment strategies for PSP.

A Cost-Effective Model for Predicting Recurrent Gastric Cancer Using Clinical Features.

This study used artificial intelligence techniques to identify clinical cancer biomarkers for recurrent gastric cancer survivors. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random Forest using MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged the synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive exPlanations (SHAPs) for feature importance analysis in this study. Our proposed Random Forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86%, and an F1 of 88.2% on the recurrent category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI (body mass index), and gender; these features significantly affect the prediction model's output and are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System.

Background: The scarcity of medical resources and personnel has worsened due to COVID-19. Telemedicine faces challenges in assessing wounds without physical examination. Evaluating pressure injuries is time consuming, energy intensive, and inconsistent. Most of today's telemedicine platforms utilize graphical user interfaces with complex operational procedures and limited channels for information dissemination. The study aims to establish a smart telemedicine diagnosis system based on YOLOv7 and large language model. Methods: The YOLOv7 model is trained using a clinical data set, with data augmentation techniques employed to enhance the data set to identify six types of pressure injury images. The established system features a front-end interface that includes responsive web design and a chatbot with ChatGPT, and it is integrated with a database for personal information management. Results: This research provides a practical pressure injury staging classification model with an average F1 score of 0.9238. The system remotely provides real-time accurate diagnoses and prescriptions, guiding patients to seek various medical help levels based on symptom severity. Conclusions: This study establishes a smart telemedicine auxiliary diagnosis system based on the YOLOv7 model, which possesses capabilities for classification and real-time detection. During teleconsultations, it provides immediate and accurate diagnostic information and prescription recommendations and seeks various medical assistance based on the severity of symptoms. Through the setup of a chatbot with ChatGPT, different users can quickly achieve their respective objectives.

Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP.

Chronic kidney disease (CKD) is a multifactorial, complex condition that requires proper management to slow its progression. In Thailand, 11.6 million people (17.5%) have CKD, with 5.7 million (8.6%) in the advanced stages and >100,000 requiring hemodialysis (2020 report). This study aimed to develop a risk prediction model for CKD in Thailand. Data from 17,100 patients were collected to screen for 14 independent variables selected as risk factors, using the IBK, Random Tree, Decision Table, J48, and Random Forest models to train the predictive models. In addition, we address the unbalanced category issue using the synthetic minority oversampling technique (SMOTE). The indicators of performance include classification accuracy, sensitivity, specificity, and precision. This study achieved an accuracy rate of 92.1% with the top-performing Random Forest model. Moreover, our empirical findings substantiate previous research through highlighting the significance of serum albumin, blood urea nitrogen, age, direct bilirubin, and glucose. Furthermore, this study used the SHapley Additive exPlanations approach to analyze the attributes of the top six critical factors and then extended the comparison to include dual-attribute factors. Finally, our proposed machine learning technique can be used to evaluate the effectiveness of these risk factors and assist in the development of future personalized treatment.

A novel therapeutic approach for endometriosis using adipose-derived stem cell-derived conditioned medium- A new hope for endometriotic patients in improving fertility.

Introduction: Endometriosis is defined as the growth of endometrial glands and stromal cells in a heterotopic location with immune dysregulation. It usually leads to chronic pelvic pain and subfertility. Although various treatments are available, the recurrence rate remains high. Adipose tissue is an abundant source of multipotent mesenchymal adipose-derived stem cells (ADSCs). ADSCs display effects on not only tissue regeneration, but also immune regulation. Thus, the current study aims to test the effects of ADSCs on the growth of endometriosis. Methods: ADSCs isolated from lipoaspiration-generated adipose tissue and their conditioned medium (ADSC-CM) were subjected to quality validation, including karyotyping as well as growth promotion and sterility tests for microbial contamination under Good Tissue Practice and Good Manufacturing Practice regulations. An autologous endometriosis mouse model was established by suturing endometrial tissue to peritoneal wall followed by treating with DMEM/F12 medium, ADSC-CM, ADSCs or ADSC-CM+ADSCs for 28 days. The area of endometriotic cysts and the degree of pelvic adhesion were measured. ICAM-1, VEGF and caspase 3 expression was assessed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry. Moreover, the mice were allowed to mate and deliver. The pregnancy outcomes were recorded. The ADSC-CM was subjected to proteomics analysis with further data mining with Ingenuity Pathway Analysis (IPA). Results: Both ADSC-CM and ADSCs passed quality validation. ADSC-CM reduced the area of endometriotic cysts. The inhibition by ADSC-CM was obliterated by adding ADSCs. The presence of ADSCs with or without ADSC-CM increased the peritoneal adhesion. ADSC-CM inhibited ICAM-1 and VEGF mRNA and protein expression, whereas the addition of ADSCs not only did not inhibit by itself, but also blocked the inhibition by ADSC-CM. The resorption rate was reduced by ADSC-CM. The number of live birth/dam and the survival rate of pup at 1 week-old were both increased by ADSC-CM in mice with endometriosis. IPA demonstrated that PTX3 was potentially critical for the inhibition of endometriosis by ADSC-CM due to its anti-inflammatory and antiangiogenic properties as well as its importance in implantation. Conclusion: ADSC-CM inhibited endometriosis development and improved pregnancy outcomes in mice. Potential translation to clinical treatment for human endometriosis is expected.

Plasma levels and tissue expression of liver-type fatty acid-binding protein in patients with breast cancer.

BACKGROUND: Liver-type fatty acid-binding protein (L-FABP) is widely expressed in hepatocytes and plays a role in lipid metabolism. It has been demonstrated to be overexpressed in different types of cancer; however, few studies have investigated the association between L-FABP and breast cancer. The aim of this study was to assess the association between plasma concentrations of L-FABP in breast cancer patients and the expression of L-FABP in breast cancer tissue. METHOD: A total of 196 patients with breast cancer and 57 age-matched control subjects were studied. Plasma L-FABP concentrations were measured using ELISA in both groups. The expression of L-FABP in breast cancer tissue was examined using immunohistochemistry. RESULT: The patients had higher plasma L-FABP levels than the controls (7.6 ng/mL (interquartile range 5.2-12.1) vs. 6.3 ng/mL (interquartile range 5.3-8.5), p = 0.008). Multiple logistic regression analysis showed an independent association between L-FABP and breast cancer, even after adjusting for known biomarkers. Moreover, the rates of pathologic stage T2+T3+T4, clinical stage III, positive HER-2 receptor status, and negative estrogen receptor status were significantly higher in the patients with an L-FABP level greater than the median. Furthermore, the L-FABP level gradually increased with the increasing stage. In addition, L-FABP was detected in the cytoplasm, nuclear, or both cytoplasm and nuclear of all breast cancer tissue examined, not in the normal tissue. CONCLUSIONS: Plasma L-FABP levels were significantly higher in the patients with breast cancer than in the controls. In addition, L-FABP was expressed in breast cancer tissue, which suggests that L-FABP may be involved in the pathogenesis of breast cancer.

Enhanced Authenticated Key Agreement for Surgical Applications in a Tactile Internet Environment.

The Tactile Internet enables physical touch to be transmitted over the Internet. In the context of electronic medicine, an authenticated key agreement for the Tactile Internet allows surgeons to perform operations via robotic systems and receive tactile feedback from remote patients. The fifth generation of networks has completely changed the network space and has increased the efficiency of the Tactile Internet with its ultra-low latency, high data rates, and reliable connectivity. However, inappropriate and insecure authentication key agreements for the Tactile Internet may cause misjudgment and improper operation by medical staff, endangering the life of patients. In 2021, Kamil et al. developed a novel and lightweight authenticated key agreement scheme that is suitable for remote surgery applications in the Tactile Internet environment. However, their scheme directly encrypts communication messages with constant secret keys and directly stores secret keys in the verifier table, making the scheme vulnerable to possible attacks. Therefore, in this investigation, we discuss the limitations of the scheme proposed by Kamil scheme and present an enhanced scheme. The enhanced scheme is developed using a one-time key to protect communication messages, whereas the verifier table is protected with a secret gateway key to mitigate the mentioned limitations. The enhanced scheme is proven secure against possible attacks, providing more security functionalities than similar schemes and retaining a lightweight computational cost.

Neuroprotective Effect of Abelmoschus manihot Flower Extracts against the H2O2-Induced Cytotoxicity, Oxidative Stress and Inflammation in PC12 Cells.

The progression of neurodegenerative diseases is associated with oxidative stress and inflammatory responses. Abelmoschus manihot L. flower (AMf) has been shown to possess excellent antioxidant and anti-inflammatory activities. This study investigated the protective effect of ethanolic extract (AME), water extract (AMW) and supercritical extract (AMS) of AMf on PC12 neuronal cells under hydrogen peroxide (H2O2) stimulation. This study also explored the molecular mechanism underlying the protective effect of AME, which was the best among the three extracts. The experimental results showed that even at a concentration of 500 µg/mL, neither AME nor AMW showed toxic effects on PC12 cells, while AMS caused about 10% cell death. AME has the most protective effect on apoptosis of PC12 cells stimulated with 0.5 mM H2O2. This is evident by the finding when PC12 cells were treated with 500 µg/mL AME; the viability was restored from 58.7% to 80.6% in the Treatment mode (p < 0.001) and from 59.1% to 98.1% in the Prevention mode (p < 0.001). Under the stimulation of H2O2, AME significantly up-regulated the expression of antioxidant enzymes, such as catalase, glutathione peroxidase and superoxide dismutase; promoted the production of the intracellular antioxidant; reduced glutathione; and reduced ROS generation in PC12 cells. When the acute inflammation was induced under the H2O2 stimulation, AME significantly down-regulated the pro-inflammatory cytokines and mediators (e.g., TNF-α, IL-1ß, IL-6, COX-2 and iNOS). AME pretreatment could also greatly promote the production of nucleotide excision repair (NER)-related proteins, which were down-regulated by H2O2. This finding indicates that AME could repair DNA damage caused by oxidative stress. Results from this study demonstrate that AME has the potential to delay the onset and progression of oxidative stress-induced neurodegenerative diseases.

Suppressive Effects of Siegesbeckia orientalis Ethanolic Extract on Proliferation and Migration of Hepatocellular Carcinoma Cells through Promoting Oxidative Stress, Apoptosis and Inflammatory Responses.

Previous studies have demonstrated that Siegesbeckia orientalis (SO) has a suppressive effect on the growth and migration of endometrial and cervical cancer cells. The present study examined the effect of SO ethanolic extract (SOE) on the proliferation and migration of hepatocellular carcinoma (HCC) and examined the effects of SOE on non-cancerous cells using HaCaT keratinocytes as a model. The SOE effectively inhibited the proliferation of Hepa1-6 (IC50 = 282.4 µg/mL) and HepG2 (IC50 = 344.3 µg/mL) hepatoma cells, whereas it has less cytotoxic effect on HaCaT cells (IC50 = 892.4 µg/mL). The SOE treatment increased the generation of ROS in HCC, but decreased the expression of antioxidant enzymes such as superoxide dismutase, glutathione peroxidase and catalase. In contrast, it reduced intracellular ROS formation and upregulated the expression of the related antioxidant enzymes in the H2O2-stimulated HaCaT cells. The SOE intervention also down-regulated the anti-apoptotic Bcl-2 and the migration-related proteins including matrix metalloproteinases (MMPs) and ß-catenin in the HCC, suggesting that SOE could promote HCC apoptosis and inhibit HCC migration. On the contrary, it reduced apoptosis and promoted the migration of the keratinocytes. Additionally, the SOE treatment significantly up-regulated the pro-inflammatory cytokines, including TNF-α, IL-6 and IL-1ß, in Hepa1-6 and HepG2 cells. Conversely, it significantly decreased the expression of these cytokines in the H2O2-induced HaCaT cells. These findings indicated that SOE treatment can delay the progression of HCC by increasing oxidative stress, promoting inflammatory response, inducing cancer cell apoptosis and inhibiting their migration. It also has protective effects from pro-oxidant H2O2 in non-cancerous cells. Therefore, SOE may provide a potential treatment for liver cancer.

Big Data, Decision Models, and Public Health.

As the digital era unfolds, the volume and velocity of environmental, population, and public health data are rapidly increasing [...].

The three-year evolution of overactive bladder syndrome in community-dwelling female residents aged 40 years and above.

OBJECTIVE: In this 3-year longitudinal cohort study, we aimed to evaluate the evolution of overactive bladder in female community residents aged 40 years and above in central Taiwan and identify its risk factors. MATERIALS AND METHODS: Female community residents aged 40 years and above were invited to participate in this study and fill out a yearly Overactive Bladder Symptom Score (OABSS) questionnaire over a 3-year period. A woman was defined to have OAB if the total OABSS was ≧4 and urgency score was ≧2. At the end of the third year, the incidence, remission, persistence, and relapse of OAB in these community residents were calculated. A novel statistical analysis technique, machine learning with data mining, was applied to examine its use in this field. Five machine learning models were used to predict the risk factors associated with persistent OAB and the results were compared with the conventional logistic regression model. RESULTS: In total, 1469 female residents were included in the first year and 1290 (87.8%) women completed the questionnaires for all 3 years. The prevalence of OAB was 20.2% (n = 260). The second- and third-year incidence rates of OAB were 13.5% and 7.1%. The remission rates were 39.6% and 44.3%. Twenty-two percent of the women reported relapse of OAB in the third year. The two-year OAB persistence rate was 43.8%. For the prediction of risk factors for persistent OAB, the multivariable logistic regression model had better predictive accuracy (AUC = 0.664) than the five machine learning models. Age â‰§ 60 was associated with persistent OAB (OR 2.8; 95% CI: 1.34-5.89, P = 0.002). CONCLUSION: The yearly incidence, remission, and persistence rates of OAB were high in female community residents aged 40 years and above in central Taiwan. Older women had a higher risk of persistent OAB symptoms in this 3-year longitudinal cohort study.

Effects of Abelmoschus manihot Flower Extract on Enhancing Sexual Arousal and Reproductive Performance in Zebrafish.

The flower of Abelmoschus manihot L. is mainly used for the treatment of chronic kidney diseases, and has been reported to have bioactivities such as antioxidant, anti-inflammatory, antiviral, and antidepressant activities. This study used wild-type adult zebrafish as an animal model to elucidate the potential bioactivity of A. manihot flower ethanol extract (AME) in enhancing their sexual and reproductive functions. Zebrafish were fed AME twice a day at doses of 0.2%, 1%, and 10% for 28 days, and were then given the normal feed for an additional 14 days. The hormone 17-ß estradiol was used as the positive control. Sexual behavioral parameters such as the number of times males chased female fish, the production of fertilized eggs, and the hatching rate of the fertilized eggs were recorded at days 0.33, 7, 14, 21, 28, and 42. The expression levels of sex-related genes­including lhcgr, ar, cyp19a1a, and cyp19a1b­were also examined. The results showed that the chasing number, fertilized egg production, and hatching rate were all increased with the increase in the AME treatment dose and treatment time. After feeding with 1% and 10% AME for 28 days, the chasing number in the treated group as compared to the control group increased by 1.52 times and 1.64 times, respectively; the yield of fertilized eggs increased by 1.59 times and 2.31 times, respectively; and the hatching rate increased by 1.26 times and 1.69 times, respectively. All three parameters exhibited strong linear correlations with one another (p < 0.001). The expression of all four genes was also upregulated with increasing AME dose and treatment duration. When feeding with 0.2%, 1%, and 10% AME for 28 days, the four sex-related genes were upregulated at ranges of 1.79−2.08-fold, 2.74−3.73-fold, and 3.30−4.66-fold, respectively. Furthermore, the effect of AME was persistent, as the promotion effect continued after the treatment was stopped for at least two weeks. The present findings suggest that AME can enhance the endocrine system and may improve libido and reproductive performance in zebrafish.

Combinatory effects of current regimens and Guizhi Fuling Wan on the development of endometriosis.

OBJECTIVE: Endometriosis, defined as the growth of endometrial glands and stromal cells in a heterotopic location under the cyclic influence of ovarian hormones, is a common gynecological disorder manifested by chronic pelvic pain and infertility. In traditional Chinese medicine, endometriosis is characterized by stagnation of vital energy (qi) and blood stasis. Guizhi Fuling Wan (GFW) was first described in Chinese canonical medicine to treat disorders associated with stagnation of qi and blood stasis, including endometriosis. Therefore, the current study aimed to test the effects of combining GFW with western medicine on the suppression of endometriosis. MATERIALS AND METHODS: Endometriosis was generated by suturing endometrial tissue on the peritoneal wall of C57BL/6JNarl mice. The mice were subsequently treated with either GFW or current hormonal therapies or in combination for 28 days. RESULTS: Endometriosis development was inhibited by GFW, Gestrinone, Visanne, GFW + Gestrinone or GFW + medroxyprogesterone acetate (MPA). The expression of intercellular adhesion molecule 1 (ICAM-1) was inhibited by GFW, Gestrinone, MPA, Visanne, GFW + Gestrinone, GFW + MPA and GFW + Visanne. Vascular endothelial growth factor (VEGF) expression was inhibited by GFW, Gestrinone, Visanne, GFW + Gestrinone and GFW + MPA. Both ICAM-1- and VEGF-reducing effects of GFW were attenuated by western medicines. Administration of GFW, MPA, Visanne, GFW + MPA and GFW + Visanne also correspondingly reduced macrophage population in peritoneal fluid. GFW, MPA, Visanne, GFW + MPA and GFW + Visanne enhanced B-cell population in peritoneal fluid. CONCLUSION: The current study reveals the therapeutic effects of GFW on endometriosis. However, the combination of GFW and current hormonal therapies potentially impedes the efficacy of each individual agent in treating endometriosis.

Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease.

Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.