cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29257370.v1
title: Comparing efficacy of different scoring models to predict hepatic encephalopathy after TIPS in cirrhotic patients

description: Assessing hepatic encephalopathy (HE) risk post-transjugular intrahepatic portosystemic shunt (TIPS) in cirrhotic patients is crucial. This study compares the predictive performance of Child-Pugh and Model for End-Stage Liver Disease (MELD), CLIFC-AD and Freiburg index of post-TIPS survival (FIPS) scores for overt and severe HE. To compare the predictive value of Child-Pugh, MELD, CLIFC-AD and FIPS scores for overt and severe HE post-TIPS in cirrhotic patients. We retrospectively analysed data from 406 cirrhotic TIPS patients (January 2017–January 2021). Scoring models were assessed for differentiation (C-index), calibration, clinical utility and overall performance at 1, 3, 6 and 12 months post-TIPS. Predictive performance for overt HE post-TIPS was low across models. FIPS had superior predictive ability for severe HE at 1 and 12 months post-TIPS (C-index: 0.781, 0.705). FIPS and CLIFC-AD showed good predictive capacity for severe HE in sarcopenic patients at 1 and 12 months (FIPS: C-index 0.863, 0.757; CLIFC-AD: C-index 0.748, 0.732). FIPS had the highest hazard ratio for severe HE (HR = 3.520, 95% CI: 2.134–5.807) and CLIFC-AD for overt HE (HR = 2.132, 95% CI: 1.581–2.874). FIPS and CLIFC-AD scores demonstrate significant predictive ability for severe HE post-TIPS, particularly in sarcopenic patients. The study compares Child-Pugh, MELD, CLIFC-AD and FIPS scores for predicting hepatic encephalopathy post-TIPS.FIPS excels in predicting post-TIPS severe hepatic encephalopathy.FIPS and CLIFC-AD scores effectively predict severe hepatic encephalopathy in sarcopenic patients. The study compares Child-Pugh, MELD, CLIFC-AD and FIPS scores for predicting hepatic encephalopathy post-TIPS. FIPS excels in predicting post-TIPS severe hepatic encephalopathy. FIPS and CLIFC-AD scores effectively predict severe hepatic encephalopathy in sarcopenic patients.
DOI: 10.6084/m9.figshare.29644155
title: Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis

description: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear. To evaluate the performance of MRI radiomics model for differentiating symptom heterogeneity of extrinsic adenomyosis, pain, abnormal uterine bleeding (AUB), infertility, and no symptom. This retrospective analysis included 405 patients with MRI-diagnosed extrinsic adenomyosis (January 2020-July 2022), randomly split 7:3 into training and test cohorts. Radiomic features were extracted from MRI-T2 image. Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. Multivariable logistic regression assessed clinical characteristics. A combined radiomics-clinical nomogram was created for symptom stratification. In total 405 patients presented with 496 clinical symptoms. In the training and test cohorts, radiomics models achieved areas under the curve (AUCs) of 0.73/0.72 (pain), 0.82/0.76 (AUB), 0.84/0.80 (infertility), and 0.80/0.71 (no symptom). The multi-signature model (radiomic + clinical features) showed improved performance, with the nomogram demonstrating good stratification ability: AUCs of 0.78/0.78 (pain), 0.87/0.85 (AUB), 0.89/0.88 (infertility), and 0.84/0.81 (no symptom) in the training/test cohort. We identified the correlation between key radiomic features and clinical symptom of extrinsic adenomyosis. The machine learning-based MRI radiomics models have potential for symptom stratification of extrinsic adenomyosis and may potentially reduce unnecessary treatment.
DOI: 10.6084/m9.figshare.29644155.v1
title: Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis

description: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear. To evaluate the performance of MRI radiomics model for differentiating symptom heterogeneity of extrinsic adenomyosis, pain, abnormal uterine bleeding (AUB), infertility, and no symptom. This retrospective analysis included 405 patients with MRI-diagnosed extrinsic adenomyosis (January 2020-July 2022), randomly split 7:3 into training and test cohorts. Radiomic features were extracted from MRI-T2 image. Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. Multivariable logistic regression assessed clinical characteristics. A combined radiomics-clinical nomogram was created for symptom stratification. In total 405 patients presented with 496 clinical symptoms. In the training and test cohorts, radiomics models achieved areas under the curve (AUCs) of 0.73/0.72 (pain), 0.82/0.76 (AUB), 0.84/0.80 (infertility), and 0.80/0.71 (no symptom). The multi-signature model (radiomic + clinical features) showed improved performance, with the nomogram demonstrating good stratification ability: AUCs of 0.78/0.78 (pain), 0.87/0.85 (AUB), 0.89/0.88 (infertility), and 0.84/0.81 (no symptom) in the training/test cohort. We identified the correlation between key radiomic features and clinical symptom of extrinsic adenomyosis. The machine learning-based MRI radiomics models have potential for symptom stratification of extrinsic adenomyosis and may potentially reduce unnecessary treatment.
DOI: 10.6084/m9.figshare.29330079
title: Prevalence of genetic alterations in basal cell carcinoma patients resistant to Hedgehog pathway inhibitors: a systematic review

description: Basal cell carcinoma (BCC) is a prevalent form of skin cancer that can be localized or metastatic. Current evidence supports the use of Hedgehog (Hh) pathway inhibitors for locally advanced or metastatic BCC with resistance due to genetic alterations in the Hh pathway. This systematic review evaluated the prevalence of genetic alterations in Hh pathway genes in BCC. We conducted a comprehensive search across four databases: PubMed, EMBASE, SCOPUS and the Cochrane Library. We included articles reporting genetic alterations in patients with locally advanced or metastatic BCC resistant to Hh pathway inhibitors. We included three prospective cohort studies encompassing 27 samples, all of which were resistant to vismodegib treatment. The most prevalent genetic mutations in the Hh pathway were in <i>PTCH1</i>, <i>SMO</i> and <i>TP53</i>, with a pooled prevalence of 44.44%. This systematic review highlights the prevalence of genetic alterations in the Hh pathway in BCC and offers insights into the mechanisms involved in treatment resistance. Understanding the high resistance rates of these genes may facilitate the development of more effective targeted therapies for BCC. The recent literature found no study established the prevalence and genetic alterations which related systemic treatments in treating advanced basal cell carcinoma. This study found that <i>PTCH1</i>, <i>SMO</i> and <i>TP53</i> were the most prevalent genetic mutations in the Hedgehog pathway. As a result, our results may influence further therapeutic strategies, resulting in disruption of the resistance mechanisms.
DOI: 10.6084/m9.figshare.29330079.v1
title: Prevalence of genetic alterations in basal cell carcinoma patients resistant to Hedgehog pathway inhibitors: a systematic review

description: Basal cell carcinoma (BCC) is a prevalent form of skin cancer that can be localized or metastatic. Current evidence supports the use of Hedgehog (Hh) pathway inhibitors for locally advanced or metastatic BCC with resistance due to genetic alterations in the Hh pathway. This systematic review evaluated the prevalence of genetic alterations in Hh pathway genes in BCC. We conducted a comprehensive search across four databases: PubMed, EMBASE, SCOPUS and the Cochrane Library. We included articles reporting genetic alterations in patients with locally advanced or metastatic BCC resistant to Hh pathway inhibitors. We included three prospective cohort studies encompassing 27 samples, all of which were resistant to vismodegib treatment. The most prevalent genetic mutations in the Hh pathway were in <i>PTCH1</i>, <i>SMO</i> and <i>TP53</i>, with a pooled prevalence of 44.44%. This systematic review highlights the prevalence of genetic alterations in the Hh pathway in BCC and offers insights into the mechanisms involved in treatment resistance. Understanding the high resistance rates of these genes may facilitate the development of more effective targeted therapies for BCC. The recent literature found no study established the prevalence and genetic alterations which related systemic treatments in treating advanced basal cell carcinoma. This study found that <i>PTCH1</i>, <i>SMO</i> and <i>TP53</i> were the most prevalent genetic mutations in the Hedgehog pathway. As a result, our results may influence further therapeutic strategies, resulting in disruption of the resistance mechanisms.
DOI: 10.6084/m9.figshare.30691935
title: A prediction model to discriminate severe fever with thrombocytopenia syndrome with hemorrhagic fever with renal syndrome

description: The Bunyavirales order encompasses hantavirus and severe fever with thrombocytopenia syndrome virus (SFTSV), which could cause hemorrhagic fever with renal syndrome (HFRS) and severe fever with thrombocytopenia syndrome (SFTS), respectively. Both are types of viral hemorrhagic fever (VHF), posing challenges for early differentiation. We focused on identifying predictors for two diseases, assisting clinicians to make diagnosis. We conducted a retrospective analysis of clinical records from patients with SFTS and HFRS patients who were hospitalized at Qingdao No. 6 People’s Hospital and Yantai Qishan Hospital between 2021 and 2023. Independent factors were explored by logistic regression and Lasso regression analysis, following a model was established. The model’s performance was carried out by the receiver operating characteristic curve (ROC) and the area under the curve (AUC), with predictors visualized by nomogram and clinical benefit assessed by decision curve analysis. Our study included 129 SFTS patients and 89 HFRS patients. Independent predictors included headache (OR: 0.098, 95% CI: 0.015–0.624, <i>p</i> = 0.014), conjunctival congestion (OR: 0.021, 95% CI: 0.002–0.253, <i>p</i> = 0.002), mucosal hemorrhage (OR: 0.003, 95% CI: 0.000–0.049, <i>p </i>&lt; 0.001), white blood cell count (WBC): 4–10 (OR: 0.019, 95% CI: 0.002–0.186, <i>p</i> = 0.001), WBC &gt; 10 (OR: 0.011, 95% CI: 0.001–0.132, <i>p</i> &lt; 0.001), CD4+ T cells ≥500 (OR: 0.013, 95% CI: 0.001–0.127, <i>p</i> &lt; 0.001). WBC had the strongest predictive power (AUC: 0.916, <i>p </i>&lt; 0.001). The model had optimal predictive ability and clinical net benefit, with an AUC of 0.988. Effective predictors were CD4+ T cells, WBC (elevated in HFRS, decreased in SFTS), headache and hemorrhage symptoms.
DOI: 10.6084/m9.figshare.30691935.v1
title: A prediction model to discriminate severe fever with thrombocytopenia syndrome with hemorrhagic fever with renal syndrome

description: The Bunyavirales order encompasses hantavirus and severe fever with thrombocytopenia syndrome virus (SFTSV), which could cause hemorrhagic fever with renal syndrome (HFRS) and severe fever with thrombocytopenia syndrome (SFTS), respectively. Both are types of viral hemorrhagic fever (VHF), posing challenges for early differentiation. We focused on identifying predictors for two diseases, assisting clinicians to make diagnosis. We conducted a retrospective analysis of clinical records from patients with SFTS and HFRS patients who were hospitalized at Qingdao No. 6 People’s Hospital and Yantai Qishan Hospital between 2021 and 2023. Independent factors were explored by logistic regression and Lasso regression analysis, following a model was established. The model’s performance was carried out by the receiver operating characteristic curve (ROC) and the area under the curve (AUC), with predictors visualized by nomogram and clinical benefit assessed by decision curve analysis. Our study included 129 SFTS patients and 89 HFRS patients. Independent predictors included headache (OR: 0.098, 95% CI: 0.015–0.624, <i>p</i> = 0.014), conjunctival congestion (OR: 0.021, 95% CI: 0.002–0.253, <i>p</i> = 0.002), mucosal hemorrhage (OR: 0.003, 95% CI: 0.000–0.049, <i>p </i>&lt; 0.001), white blood cell count (WBC): 4–10 (OR: 0.019, 95% CI: 0.002–0.186, <i>p</i> = 0.001), WBC &gt; 10 (OR: 0.011, 95% CI: 0.001–0.132, <i>p</i> &lt; 0.001), CD4+ T cells ≥500 (OR: 0.013, 95% CI: 0.001–0.127, <i>p</i> &lt; 0.001). WBC had the strongest predictive power (AUC: 0.916, <i>p </i>&lt; 0.001). The model had optimal predictive ability and clinical net benefit, with an AUC of 0.988. Effective predictors were CD4+ T cells, WBC (elevated in HFRS, decreased in SFTS), headache and hemorrhage symptoms.
DOI: 10.6084/m9.figshare.28368982
title: The extent of intestinal involvement is closely related to the severity of IgAV: a risk stratification study based on CT

description: To analyze the differences in clinical manifestations between children and adults with intestinal involvement in IgAV and to identify the specific subtypes requiring particular attention. A systematic review of the HIS system was conducted on patient data from four centers at Tongji Hospital between January 2015 and October 2022. Patients with a diagnosis of IgAV with intestinal involvement were further analyzed. Clinical manifestations, laboratory data, and CT findings at the time of initial hospitalization were recorded. The differences in clinical manifestations between children and adults were analyzed. The extent of intestinal involvement, linked to disease severity, was quantitatively assessed by evaluating the number of affected intestinal segments on CT imaging. Laboratory markers that could reflect severe intestinal involvement were explored. Furthermore, patients were classified based on the sites of involved intestinal segments: L1 type (duodenum and/or jejunum), L2 type (ileum), L3 type (duodenum and/or jejunum and ileum), and L4 type (limited to the colorectum). A comparison of the first three types was performed. A total of 148 patients were enrolled (67 children and 81 adults). The proportion of joint pain and renal involvement was higher in adults. D-dimer level was an independent risk factor for severe intestinal involvement (OR = 1.104, <i>p</i> = .016). In the first three types patients based on the sites of involved intestinal segments, it found that L3 type patients had a longer hospital stay. With the exception of joint pain and renal involvement, there were no significant differences in clinical symptoms between children and adults. CT imaging provided objective insights into the extent of intestinal involvement, which correlated with disease severity. Patients with widespread small bowel involvement displayed a more severe disease state.
DOI: 10.6084/m9.figshare.28368982.v1
title: The extent of intestinal involvement is closely related to the severity of IgAV: a risk stratification study based on CT

description: To analyze the differences in clinical manifestations between children and adults with intestinal involvement in IgAV and to identify the specific subtypes requiring particular attention. A systematic review of the HIS system was conducted on patient data from four centers at Tongji Hospital between January 2015 and October 2022. Patients with a diagnosis of IgAV with intestinal involvement were further analyzed. Clinical manifestations, laboratory data, and CT findings at the time of initial hospitalization were recorded. The differences in clinical manifestations between children and adults were analyzed. The extent of intestinal involvement, linked to disease severity, was quantitatively assessed by evaluating the number of affected intestinal segments on CT imaging. Laboratory markers that could reflect severe intestinal involvement were explored. Furthermore, patients were classified based on the sites of involved intestinal segments: L1 type (duodenum and/or jejunum), L2 type (ileum), L3 type (duodenum and/or jejunum and ileum), and L4 type (limited to the colorectum). A comparison of the first three types was performed. A total of 148 patients were enrolled (67 children and 81 adults). The proportion of joint pain and renal involvement was higher in adults. D-dimer level was an independent risk factor for severe intestinal involvement (OR = 1.104, <i>p</i> = .016). In the first three types patients based on the sites of involved intestinal segments, it found that L3 type patients had a longer hospital stay. With the exception of joint pain and renal involvement, there were no significant differences in clinical symptoms between children and adults. CT imaging provided objective insights into the extent of intestinal involvement, which correlated with disease severity. Patients with widespread small bowel involvement displayed a more severe disease state.
DOI: 10.6084/m9.figshare.29972938
title: Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study

description: To explore the mechanisms of acupuncture-induced neck pain relief and identify appropriate candidates using neuroimaging and machine learning techniques. Eighty neck pain patients were included, with clinical data and functional magnetic resonance imaging scans collected pre- and post-treatment. A support vector machine (SVM) model was built using pre-treatment brain functional connectivity to predict acupuncture responsiveness, identifying key features as potential biomarkers for effectiveness. Longitudinal analysis of these features was conducted in responders and non-responders. This study enrolled 80 neck pain patients (48 acupuncture responders and 32 non-responders) for SVM model construction and longitudinal analysis of predictive features pre-/post-treatment. The SVM model achieved an accuracy of 0.85 in distinguishing the two groups. A total of 117 functional connectivity edges were identified as predictive features, potential biomarkers for acupuncture responses. Longitudinal analysis showed 6 predictive features altered post-treatment in responders versus 44 in non-responders. After FDR correction, only 3 functional connectivity features in responders negatively correlated with pain VAS scores (<i>p</i> &lt; 0.05). These findings indicate more targeted changes in predictive features among responders compared to non-responders. Using pre-treatment neuroimaging features to predict acupuncture effectiveness for neck pain shows promise. This approach could aid in developing personalized acupuncture strategies by identifying likely beneficiaries, guiding alternative interventions for non-responders. International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001, protocol version number: V1.0)
DOI: 10.6084/m9.figshare.29972938.v1
title: Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study

description: To explore the mechanisms of acupuncture-induced neck pain relief and identify appropriate candidates using neuroimaging and machine learning techniques. Eighty neck pain patients were included, with clinical data and functional magnetic resonance imaging scans collected pre- and post-treatment. A support vector machine (SVM) model was built using pre-treatment brain functional connectivity to predict acupuncture responsiveness, identifying key features as potential biomarkers for effectiveness. Longitudinal analysis of these features was conducted in responders and non-responders. This study enrolled 80 neck pain patients (48 acupuncture responders and 32 non-responders) for SVM model construction and longitudinal analysis of predictive features pre-/post-treatment. The SVM model achieved an accuracy of 0.85 in distinguishing the two groups. A total of 117 functional connectivity edges were identified as predictive features, potential biomarkers for acupuncture responses. Longitudinal analysis showed 6 predictive features altered post-treatment in responders versus 44 in non-responders. After FDR correction, only 3 functional connectivity features in responders negatively correlated with pain VAS scores (<i>p</i> &lt; 0.05). These findings indicate more targeted changes in predictive features among responders compared to non-responders. Using pre-treatment neuroimaging features to predict acupuncture effectiveness for neck pain shows promise. This approach could aid in developing personalized acupuncture strategies by identifying likely beneficiaries, guiding alternative interventions for non-responders. International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001, protocol version number: V1.0)
DOI: 10.6084/m9.figshare.29321174.v1
title: Back to the pre-industrial age? FAOSTAT statistics of food supply reveal radical dietary changes accompanied by declining body height, rising obesity rates, and declining phenotypic IQ in affluent Western countries

description: Meta-analyses of observational and clinical studies conducted in recent years have raised serious doubts about the validity of the low-fat dietary recommendations introduced in the late 1970s/early 1980s, due to the absence of any convincing link between saturated fat and the risk of cardiovascular diseases. At the same time, long-term food supply statistics from the FAOSTAT database show that these recommendations were at the root of fundamental dietary changes in Western countries, which resulted in a lower consumption of eggs and red meat, a higher consumption of cereals and poultry, a decline in average protein quality and, overall, in a higher glycemic load of the diet. Because current views on human nutrition are based primarily on highly unreliable questionnaire data from observational studies, the purpose of this commentary is to provide an alternative ecological (country-level) perspective and to trace the consequences of these nutritional changes using the FAOSTAT database in combination with available anthropological and health statistics. This comparison shows a close connection between the decline in protein quality and the sudden reversal of the positive height trend in some Western countries, after ∼150 years of continuous growth, which points to suboptimal levels of child nutrition. The sharp increase in the prevalence of obesity and type 2 diabetes is strongly correlated with the increasing consumption of high-glycemic carbohydrates and sweeteners, and is also interconnected with the decrease in body height, because a high-quality, growth-stimulating diet during adolescence is inversely related to obesity. Given the long-term association between height and phenotypic IQ, the lower quality of nutrients in children’s diet may also seriously affect intellectual potential and future civilizational development. In light of these findings, current nutritional strategies should be seriously reconsidered and recommended protein intakes for children must be urgently reevaluated.