Impact of cachexia on disease recurrence and survival outcomes in endometrial cancer patients.

Objective: Cancer cachexia is progressive weight loss due to muscle/adipose tissue wasting and inadequate intake that occurs in response to malignancy. It is an independent predictor of disease recurrence and reduced survival in several cancers. However, cachexia's relationship with gynecologic malignancy outcomes has only been examined in small studies with limited follow-up and inconsistent definitions of cachexia. This study investigated the impact of cachexia on disease recurrence and overall survival in high-risk endometrial carcinoma patients. Methods: This retrospective cohort study examined data from patients with high-risk non-metastatic primary endometrial carcinoma treated at a single institution from 2015 to 2020. Treatment for all subjects included total hysterectomy, surgical staging, pelvic external beam radiotherapy with or without adjuvant chemotherapy. Radiation planning CT datasets were used to measure skeletal musculature at the L3 vertebral level. Skeletal muscle index (SMI) was defined as total L3 skeletal muscle cross sectional area (cm2)/height2 (m2), and cachexia was defined based on SMI. Results: 55 patients were eligible for analysis. Several SMI thresholds were used to define cachexia, and analysis was performed for each definition. Kaplan-Meier and Cox-proportional hazards regression analysis yielded no significant reduction in overall survival (OS) or progression-free survival (PFS) in patients with cachexia, regardless of threshold chosen. However, 4 of 13 definitions of cachexia showed significantly improved OS in patients without cachexia, relative to those with cachexia. There were no significant differences in disease recurrence. Conclusions: Cachexia as defined in this study was not associated with poor outcomes in endometrial carcinoma patients based on OS, PFS, or disease recurrence.

Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation.

Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model using data from 525 pediatric patients with solid tumors to predict the day when patients recover from severe neutropenia after high-dose chemotherapy. We validated the model with data from 99 patients and compared its performance to those of clinicians. The accuracy of the model at predicting the recovery day, with a 1-day error, was 76%; its performance was better than those of the specialist group (58.59%) and the resident group (32.33%). In addition, 80% of clinicians changed their initial predictions at least once after the model's prediction was conveyed to them. In total, 86 prediction changes (90.53%) improved the recovery day estimate.

Influenza Screening Using Patient-Generated Health Data in Post COVID-19 Pandemic.

It is very important to ensure reliable performance of deep learning model for future dataset for healthcare. This is more pronounced in the case of patient generated health data such as patient reported symptoms, which are not collected in a controlled environment. Since there has been a big difference in influenza incidence since the COVID-19 pandemic, we evaluated whether the deep learning model can maintain sufficiently robust performance against these changes. We have collected 226,655 episodes from 110,893 users since June 2020 and tested the influenza screening model, our model showed 87.02% sensitivity and 0.8670 of AUROC. The results of COVID-19 pandemic are comparable to that of before COVID-19 pandemic.

Symptom-Based COVID19 Screening Model Combined with Surveillance Information.

As the number of cases for COVID-19 continues to grow unprecedentedly, COVID-19 screening is becoming more important. In this study, we trained machine learning models from the Israel COVID-19 dataset and compared models that used surveillance indices of COVID-19 and those that did not. The AUC scores were 0.8478±0.0037 and 0.8062±0.005 with and without surveillance information, respectively, and there was significant improvement when the surveillance information was used.

Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study.

BACKGROUND: Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. OBJECTIVE: The aim of this study was to develop a machine learning-based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. METHODS: We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient's fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user's age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. RESULTS: We achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). CONCLUSIONS: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions.

Electroencephalographic frontal synchrony and caudal asynchrony during painful hand immersion in cold water.

Recent studies in our laboratory showed that cortical theta oscillations correlate with pain in rodent models. In this study, we sought to validate our pre-clinical data using EEG recordings in humans during immersion of the hand in ice cold water, a moderately noxious stimulus. Power spectral analysis shows that an increase in pain score is associated with an increase in power amplitude within a frequency range of 6-7Hz at the frontal (Fz) electrode. These results are consistent with our previous pre-clinical animal studies and the clinical literature. We also report a novel reduction in power at the caudal (O1) electrode within a broader 3-30Hz rand and decreased coherence between Fz and C3, C4 electrodes within the theta (4-8Hz) and low beta (13-21Hz) bands, while coherence (an indirect measure of functional connectivity) between Fz and O1 increased within the theta and alpha (8-12Hz) bands. We argue that pain is associated with EEG frontal synchrony and caudal asynchrony, leading to the disruption of cortico-cortical connectivity.

Combined treatment with acupuncture reduces effective dose and alleviates adverse effect of L-dopa by normalizing Parkinson's disease-induced neurochemical imbalance.

This study first showed the behavioural benefits of novel combination therapy of L-dopa with acupuncture on Parkinson's disease, and its underlying mechanisms within basal ganglia. The previous study reported that acupuncture may improve the motor function of a Parkinson's disease (PD) mouse model by increasing the dopamine efflux and turnover ratio of dopamine. Hence, we hypothesised that combining L-dopa with acupuncture would have a behavioural benefit for those with PD. We performed unilateral injections of 6-OHDA into the striatum of C57Bl/6 mice to model hemi-Parkinsonian attributes. To test motor function and dyskinetic anomalies, we examined cylinder behaviour and abnormal involuntary movement (AIM), respectively. We found that (1) a 50% reduced dose of L-dopa (7.5 mg/kg) combined with acupuncture showed an improvement in motor function that was comparable to mice given the standard dose of L-dopa treatment (15 mg/kg) only, and that (2) the combination treatment (L-dopa +acupuncture) was significantly superior in reducing AIM scores when equivalent doses of L-dopa were used. The combination treatment also significantly reduces the abnormal increase of GABA contents in the substantia nigra compared to the standard L-dopa treatment. Furthermore, abnormal expression of FosB, the immediate early gene of L-dopa induced dyskinesia (LID), was mitigated in the striatum by the combination treatment. All of these results indicate that acupuncture enhances the benefits of L-dopa on motor function with reduced dose of L-dopa and alleviating LID by normalising neurochemical imbalance within the basal ganglia.

Infrared heat treatment reduces food intake and modifies expressions of TRPV3-POMC in the dorsal medulla of obesity prone rats.

PURPOSE: Infrared heat, a transient receptor potential vanilloid type-3 (TRPV3) sensitive stimulus, may have potential physiological effects beneficial to treating metabolic syndrome. MATERIALS AND METHODS: Obesity prone (OP) and obesity resistant (OR) rats were fed for seven days on a high-fat diet. Heat treated OP rats were exposed twice daily to infrared light for 20 min each, separated by 80 min of rest. Food intake, blood pressure, blood glucose, and body weight measurements were taken daily and compared between treated OP rats, untreated OP rats, and OR controls. The animals were perfused with 4% paraformaldehyde, and immunohistochemistry was performed on the coronal brainstem sections with polyclonal antibodies against TRPV3 and pro-opiomelanocortin (POMC). The positive-staining cells in the medulla nuclei were quantified using a microscope with reticule grid. RESULTS: Food intake, body weight, and mean arterial blood pressure (MAP) were higher in OP rats, a diet-induced metabolic syndrome model, accompanied by a reduced expression of POMC, an anorectic agent, in the hypoglossal nucleus (HN) and medial nucleus tractus solitarius (mNTS). Food intake in heat-treated OP rats was significantly decreased. POMC positive neuron count was increased in the HN and mNTS of OP rats following treatment. TRPV3 positive staining neurons were increased in the HN and mNTS of OP control rats and decreased following the heat treatments. CONCLUSION: Lowered POMC and heightened TRPV3 expressions in the HN and mNTS are involved in development of hyperphagia and obesity in OP rats. Exposure to infrared heat modifies TRPV3 and POMC expression in the brainstem, reducing food intake.