BACKGROUND Coronavirus disease 2019(COVID-19)patients with malignancy are published worldwide but are lacking in data from India.AIM To characterize COVID-19 related mortality outcomes within 30 d of diagnosis with HR...BACKGROUND Coronavirus disease 2019(COVID-19)patients with malignancy are published worldwide but are lacking in data from India.AIM To characterize COVID-19 related mortality outcomes within 30 d of diagnosis with HRCT score and RT-PCR Ct value-based viral load in various solid malignancies.METHODS Patients included in this study were with an active or previous malignancy and with confirmed severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection from the institute database.We collected data on demographic details,baseline clinical conditions,medications,cancer diagnosis,treatment and the COVID-19 disease course.The primary endpoint was the association between the mortality outcome and the potential prognostic variables,specially,HRCT score,RT-PCR Ct value-based viral load,etc.using logistic regression analyses treatment received in 30 d.RESULTS Out of 131 patients,123 met inclusion criteria for our analysis.The median age was 57 years(interquartile range=19-82)while 7(5.7%)were aged 75 years or older.The most prevalent malignancies were of GUT origin 49(39.8%),hepatopancreatobiliary(HPB)40(32.5%).109(88.6%)patients were on active anticancer treatment,115(93.5%)had active(measurable)cancer.At analysis on May 20,2021,26(21.1%)patients had died.In logistic regression analysis,independent factors associated with an increased 30-d mortality were in patients with the symptomatic presentation.Chemotherapy in the last 4 wk,number of comorbidities(≥2 vs none:3.43,1.08-8.56).The univariate analysis showed that the risk of death was significantly associated with the HRCT score:for moderate(8-15)[odds ratio(OR):3.44;95%confidence interval(CI):1.3-9.12;P=0.0132],severe(>15)(OR:7.44;95%CI:1.58-35.1;P=0.0112).CONCLUSION To the best of our knowledge,this is the first study from India reporting the association of HRCT score and RT-PCR Ct value-based 30-d mortality outcomes in SARS-CoV-2 infected cancer patients.展开更多
When a person's neuromuscular system is affected by an injury or disease,Activities‐for‐Daily‐Living(ADL),such as gripping,turning,and walking,are impaired.Electroen-cephalography(EEG)and Electromyography(EMG)a...When a person's neuromuscular system is affected by an injury or disease,Activities‐for‐Daily‐Living(ADL),such as gripping,turning,and walking,are impaired.Electroen-cephalography(EEG)and Electromyography(EMG)are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject,and they are used in Brain–Computer Interface(BCI)or robotic rehabilitation systems.However,existing BCI or robotic rehabilitation systems use signal classification technique limitations such as(1)missing temporal correlation of the EEG and EMG signals in the entire window and(2)overlooking the interrelationship between different sensors in the system.Furthermore,typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions;(3)their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals.A novel classification model,named BIOFIS is proposed,which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships.It ex-plores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory(LSTM)block.The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward,grip and raise,lower and release,and reverse.The system can achieve 98.6%accuracy for a 4‐way action using EEG data and 97.18%accuracy using EMG data.Moreover,even without the dominant signal,the accuracy scores were 90.1%for the EEG data and 85.2%for the EMG data.The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.展开更多
文摘BACKGROUND Coronavirus disease 2019(COVID-19)patients with malignancy are published worldwide but are lacking in data from India.AIM To characterize COVID-19 related mortality outcomes within 30 d of diagnosis with HRCT score and RT-PCR Ct value-based viral load in various solid malignancies.METHODS Patients included in this study were with an active or previous malignancy and with confirmed severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection from the institute database.We collected data on demographic details,baseline clinical conditions,medications,cancer diagnosis,treatment and the COVID-19 disease course.The primary endpoint was the association between the mortality outcome and the potential prognostic variables,specially,HRCT score,RT-PCR Ct value-based viral load,etc.using logistic regression analyses treatment received in 30 d.RESULTS Out of 131 patients,123 met inclusion criteria for our analysis.The median age was 57 years(interquartile range=19-82)while 7(5.7%)were aged 75 years or older.The most prevalent malignancies were of GUT origin 49(39.8%),hepatopancreatobiliary(HPB)40(32.5%).109(88.6%)patients were on active anticancer treatment,115(93.5%)had active(measurable)cancer.At analysis on May 20,2021,26(21.1%)patients had died.In logistic regression analysis,independent factors associated with an increased 30-d mortality were in patients with the symptomatic presentation.Chemotherapy in the last 4 wk,number of comorbidities(≥2 vs none:3.43,1.08-8.56).The univariate analysis showed that the risk of death was significantly associated with the HRCT score:for moderate(8-15)[odds ratio(OR):3.44;95%confidence interval(CI):1.3-9.12;P=0.0132],severe(>15)(OR:7.44;95%CI:1.58-35.1;P=0.0112).CONCLUSION To the best of our knowledge,this is the first study from India reporting the association of HRCT score and RT-PCR Ct value-based 30-d mortality outcomes in SARS-CoV-2 infected cancer patients.
文摘When a person's neuromuscular system is affected by an injury or disease,Activities‐for‐Daily‐Living(ADL),such as gripping,turning,and walking,are impaired.Electroen-cephalography(EEG)and Electromyography(EMG)are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject,and they are used in Brain–Computer Interface(BCI)or robotic rehabilitation systems.However,existing BCI or robotic rehabilitation systems use signal classification technique limitations such as(1)missing temporal correlation of the EEG and EMG signals in the entire window and(2)overlooking the interrelationship between different sensors in the system.Furthermore,typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions;(3)their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals.A novel classification model,named BIOFIS is proposed,which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships.It ex-plores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory(LSTM)block.The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward,grip and raise,lower and release,and reverse.The system can achieve 98.6%accuracy for a 4‐way action using EEG data and 97.18%accuracy using EMG data.Moreover,even without the dominant signal,the accuracy scores were 90.1%for the EEG data and 85.2%for the EMG data.The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.
