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Graphical abstract, Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

Highlights • U.S. neurosurgery residents report moderate burnout during the COVID-19 pandemic. • Neurosurgery residents report uncertainty about future healthcare reform & earnings. • The COVID-19 pandemic may impair residents’ achievement of surgical milestones. • Burnout is associated with altered rotation or vacation schedules and lower PGY. • Career satisfaction is associated with delivery of neurosurgical patient care., The coronavirus disease 2019 (COVID-19) pandemic has posed significant changes to resident education and workflow. However, the impact of the pandemic on U.S. neurosurgery residents has not been well characterized. We investigated the impact of the COVID-19 pandemic on U.S. neurosurgery resident workflow, burnout, and career satisfaction. In 2020, a survey evaluating factors related to career satisfaction and burnout was emailed to 1,374 American Association of Neurological Surgeons (AANS) residents. Bivariate and multivariate (logistic) analyses were performed to characterize predictors of burnout and career satisfaction. 167 survey responses were received, with a response rate (12.2%) comparable to that of similar studies. Exclusion of incomplete responses yielded 111 complete responses. Most respondents were male (65.8%) and White (75.7%). Residents reported fewer work hours (67.6%) and concern that COVID-19 would impair their achievement of surgical milestones (65.8%). Burnout was identified in 29 (26.1%) respondents and career satisfaction in 82 (73.9%) respondents. In multivariate analysis, burnout was significantly associated with alterations in elective rotation/vacation schedules (p = .013) and the decision to not pursue neurosurgery again if given the choice (p

Background Prognostic implications of blood cholesterol may differ at different stages of life. This cohort study compares the value of total cholesterol (TC) readings earlier versus later in life for the prediction of coronary atherosclerosis, cardiovascular events, and cardiovascular death. Methods In a cardiovascular observation study (CVOS) we performed coronary angiography and prospectively recorded cardiovascular events in 1090 patients over up to 19 years. These patients had participated in a health survey (HS) 15 years prior to the CVOS baseline. TC was measured twice, first at the earlier HS and then later at CVOS recruiting. Findings Patients in the highest versus the lowest TC-category of the HS had an OR of 4.30 [2.41–7.65] for significant CAD at angiography, a HR of 1.74 [1.10–2.76] for cardiovascular events, and a HR of 7.55 [1.05–54.49] for cardiovascular death after multivariate adjustment. In contrast, TC as measured at the baseline of the CVOS was neither significantly associated with significant CAD (OR= 0.75 [0.49–1.13]) nor with cardiovascular events or death during follow-up (HR= 0.86 [0.62–1.18] and 0.79 [0.41–1.53], respectively). Moreover, the ESC/EAS-SCORE was found to be more powerful in predicting cardiovascular mortality when using earlier instead of later TC, with a continuous net reclassification improvement of 0.301 (p Interpretation Early measurement not only enables early intervention in keeping with the concept of lifelong exposure to atherogenic lipoproteins. These data also suggest that cardiovascular risk prediction is more accurate if using earlier in life TC readings. Funding The present study did not receive any particular funding

Background The novel acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic imposed a national lockdown at the beginning of 2020. People faced social distancing, being forced to stay at home. Peripheral arterial disease (PAD) typically influences life habits and psycho-social relationship. It is now questionable how PAD can be affected by changes in lifestyle imposed by the Sars-CoV-19 Pandemic. This study's main objective is to evaluate how the limitations of social behavior set by Sars-CoV-19 Pandemic impact the perception and evolution of the disease in a group of patients with a diagnosed PAD. Methods The changes in the in the perception and the evolution of the PAD were evaluated by comparing results of a modified VascuQol-6 quality of life (QoL) survey referring to the time frame defined “No-Sars-CoV-2 period” (from July to December 2019) with results referring to the time frame defined “Sars-CoV-2 period” (from January to June 2020). An overall score (range 4-60) was calculated, and a comparison between the two period studies was reported. Receiver Operating Characteristics (ROC) Curves evaluated a delta value for each patient with revascularization of lower limb peripheral arteries. Optimal cut-offs were chosen based on their specificity, sensitivity. Results One-hundred-two PAD patients gave their informed consent to take part in the study. A significant general worsening of patients PAD perception in the Sars-CoV-2 period was recorded for the following items: lower limbs health status perception; overall activity limitation; walking ability limitation; overall daily walking distance; lower limbs fatigue perception; concerns about PAD worsening; pain discomfort (P < 0.05). The pain intensity changed from 4.7 ± 2.9 in the No-Sars-CoV-2 period to 6.3 ± 2.9 in the Sars-CoV-2 period significantly (P < 0.0001), even though analgesic drug intake did not increase considerably in Sars-CoV-2 period (P = 0.15). The overall score was 20.3 ± 7.4 for the No-Sars-CoV-2 period and 27.4 ± 7.6 in the Sars-CoV-2 period (P = 0.0001). The ROC curve built to analyze the relation between Delta-score and the need for revascularization identifies a cut-off > 8.5 (Area Under the Curve 0.5436; CI 95% 0.4252 to 0.6620) with a sensitivity and specificity respectively of 52.6% (CI 95% 37.26–67.52%) and 65.6% (CI 95% 53.40–76.08%). Conclusions Patients with PAD were significantly hit by all the restrictions and the social limitations imposed to reduce the Sars-CoV-19 virus diffusion. Our study confirms that the perception and the evolution of PAD were significantly affected during the “Sars-CoV-2 period”.

Background Venous thromboembolism (VTE) is a frequent complication in critically ill patients with coronavirus disease 2019 (COVID-19) and is associated with mortality. Early diagnosis and treatment of VTE is warranted. Objective To develop a prediction model for VTE in critically ill COVID-19 patients. Patients and methods In this retrospective cohort study, 127 adult patients with confirmed COVID-19 infection admitted to the intensive care unit of two teaching hospitals were included. VTE was diagnosed with either ultrasound or computed tomography scan. Univariate receiver operating characteristic (ROC) curves were constructed for Positive End Expiratory Pressure, PaO2/FiO2 ratio, platelet count, international normalized ratio, activated partial thromboplastin time as well as levels of fibrinogen, antithrombin, D-dimer and C-reactive protein (CRP). Multivariate analysis was done using binary linear regression. Results Variables associated with VTE in both univariate and multivariate analysis were D-dimer and CRP with an area under the curve (AUC) of 0.64, P = 0.023 and 0.75, P = 0.045, respectively. Variables indicating hypoxemia were not predictive. The ROC curve of D-dimer and CRP combined had an AUC of 0.83, P 15 in combination with a CRP > 280 was 98%. The negative predictive value of D-dimer was low. Conclusion Elevated CRP and D-dimer have a high positive predictive value for VTE in critically ill COVID-19 patients. We developed a prediction table with these biomarkers that can aid clinicians in the timing of imaging in patients with suspected VTE., Highlights • Venous thromboembolisms are a frequently observed complication of COVID-19. • Markers of oxygenation are not predictive of venous thromboembolism. • Elevated C-reactive protein and D-dimer have the potential to predict venous thromboembolism. • We created a prediction tool based on elevations in both CRP and D-dimer to optimize time of imaging.

There has been no publication which supports the usefulness of DWI differentiating for suture recurrence and suture granuloma after resection for lung cancer. We presented efficacy of DWI or FDG-PET/CT for an assessment of suture lesions after resection for lung cancer. Thirteen suture recurrences and 15 suture granulomas were examined. There were 24 adenocarcinomas and 4 squamous cell carcinomas, and 26 partial resections and 2 segmentectomies. The period of time (907±907 days) between surgery and suture recurrence was not significantly longer than that (546±547 days) between surgery and suture granuloma. Diffusion detectability scores (a 5-point scale) of suture recurrences was significantly higher than that of suture granulomas. The ADC value (1.35±0.24 × 10−3mm2/sec) of suture recurrences was significantly lower than that (1.85±0.60 × 10−3mm2/sec) of suture granulomas. The SUVmax (6.1 ± 5.0) of suture recurrences was not significantly higher than that (4.2 ± 2.5) of suture granulmas. The sensitivity of 85% (11/13) with DWI was not significantly higher than 69% (9/13) with FDG-PET/CT for suture recurrences. The specificity of 73% (11/15) with DWI was not significantly higher than the 60% (9/15) with FDG-PET/CT for suture granulomas. The accuracy of 79% (22/28) with DWI was not significantly higher than that of 64% (18/28) with FDG-PET/CT for suture recurrences and granulomas. DWI can differentiate suture granuloma from suture recurrence after resection of lung cancer. DWI is more useful than FDG-PET/CT for the differentiation between suture recurrence and suture granuloma after resection for lung cancer.

Graphical abstract, Highlights • Motor intention based arm training targets activity-dependent neuroplasticity. • 80% of stroke participants recovered clinically relevant functional movements. • Ipsi-lesional, delta-band EEG activity was highly correlated with motor recovery. • Results suggest higher activation of ipsi-lesional hemisphere post-intervention., Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants’ movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p

Background More than 300 million people carry a diagnosis of asthma, with data to suggest that they are at a higher risk for infection or adverse outcomes from severe acute respiratory syndrome coronavirus 2. Asthma is remarkably heterogeneous, and it is currently unclear how patient-intrinsic factors may relate to coronavirus disease 2019. Objective We sought to identify and characterize subsets of patients with asthma at increased risk for severe acute respiratory syndrome coronavirus 2 infection. Methods Participants from 2 large asthma cohorts were stratified using clinically relevant parameters to identify factors related to angiotensin-converting enzyme-2 (ACE2) expression within bronchial epithelium. ACE-2–correlated gene signatures were used to interrogate publicly available databases to identify upstream signaling events and novel therapeutic targets. Results Stratifying by type 2 inflammatory biomarkers, we identified subjects who demonstrated low peripheral blood eosinophils accompanied by increased expression of the severe acute respiratory syndrome coronavirus 2 receptor ACE2 in bronchial epithelium. Genes highly correlated with ACE2 overlapped with type 1 and 2 IFN signatures, normally induced by viral infections. T-cell recruitment and activation within bronchoalveolar lavage cells of ACE2-high subjects was reciprocally increased. These patients demonstrated characteristics corresponding to risk factors for severe coronavirus disease 2019, including male sex, history of hypertension, low peripheral blood, and elevated bronchoalveolar lavage lymphocytes. Conclusions ACE2 expression is linked to upregulation of viral response genes in a subset of type 2–low patients with asthma with characteristics resembling known risk factors for severe coronavirus disease 2019. Therapies targeting the IFN family and T-cell–activating factors may therefore be of benefit in a subset of patients.

Aim and objectives: To study the diagnostic accuracy of Diffusion tensor imaging technique in detection of cervical spondylotic myelopathy changes. Material and method: Study population included 50 patients with symptoms of cervical myelopathy. The patients were evaluated based on symptoms using the European myelopathy scoring system and were divided into: Grade 1, including patients with mild symptoms; Grade 2, referring to patients with moderate symptoms and Grade 3, which included patients revealing severe symptoms. All the patients were investigated with a 1.5 T MRI unit acquiring DWI and DTI sequences. FA and ADC values from each spinal segment were analyzed in terms of Frequency, Percentage, Mean, Standard Deviation and Confidence Intervals. The comparison of values was done by ANOVA and post hoc analysis by bonferroni test. Comparison of accuracy of FA, ADC and T2WI in recognizing myelopathic changes was done by t-test. Receiver Operating Characteristics (ROC) analysis was performed to obtain a cut off value of FA and ADC for each spinal level to identify myelopathic change in the spinal cord. Results: The study revealed a significant difference in the mean FA and ADC value of stenotic and Non-stenotic segments. T2WI was highly significant (p = 0.000) in recognizing myelopathy changes in patients falling under Grade 2(moderate) and Grade 3(severe) according to European Myelopathy scoring system. Regarding patients under Grade 1 (mild) FA and ADC values showed significant difference compared to T2WI. The collective sensitivity in the identification of myelopathic changes was highest with FA (79%) as compared to ADC (71%) and T2WI (50%). ROC analysis was done to determine the cut off values of FA and ADC at each cervical spine segments. The proposed cut off, for FA and ADC at the level of C1–C2 is 0.68 and 0.92, C2–C3 is 0.65 and 1.03, C3–C4 is 0.63 and 1.01, C4–C5 0.61 and 0.98, At C5–C6 0.57 and 1.04, At C6–C7 0.56 and 0.96 respectively. Conclusion: FA and ADC values enhance the efficacy and accuracy of MRI in the diagnosis of cervical spondylotic myelopathy. Hence diffusion tensor imaging can be used as a non-invasive modality to recognize spondylotic myelopathy changes even in the early stages, which can be helpful in deciding on appropriate timing of decompression surgery before the irreversible chronic changes set in. Keywords: Diffusion tensor imaging, Cervical spondylotic myelopathy

Background Hospital acquired infection (HAI) and multiple organ dysfunctions (MODS) remain a leading cause of death in pediatric intensive care unit (PICU) despite the great efforts to control it. Objective Our objective was to assess the mRNA of TNFα and BCL2 for prediction of HAI and/or MODS in our community. Patients and methods Fifty children, admitted to PICU, were included in the study after exclusion of cases of end-stage renal failure, end-stage liver failure and congenital immune deficiency. Serial Blood samples were collected for complete blood count (CBC) and other routine investigations. Gene expression of (TNFα and BCL2) was quantified using quantitative real time PCR (qRT-PCR). Centers of disease control (CDC) criteria were used to detect HAI, and organ failure index (OFI). Pediatric logistic organ dysfunction (PELOD) and pediatric risk of mortality (PRISM) scores were used for follow up. The results were compared between the group who acquired HAI and who didn't. Gene expression was tested with a ROC curve to detect its ability to predict HAI. Main results The overall complication (HAI and/or MODS) rate was 52%, Complicated cases had a significantly longer duration of stay in PICU (0.002) and in overall hospital stay (p = 0.013) and a higher death rate (p = 0.000). On day1; TNFα, BCL2 and lymphocytic count were lower in patients who developed complications (p = 0.02, p = 0.000 and p = 0.04, respectively), all had the ability to predict the complications with AUC (0.7, 0.8 and 0.67 respectively). On day 4: TNFα and BCL2 returned to normal levels while the lymphocytic count still lower in complicated cases, p = 0.001 and AUC = 0.73. Conclusions TNFα and BCL2 on admission can predict HAI and MODS (AUC = 0.7 and AUC = 0.8), but were of no use in the follow-up, however, the lymphocytic count is a rapid, easy and cheap test to assess the immune state with a good predictive and follow up values., Highlights • To our knowledge, our work is the 1st to show mRNA biomarkers in acquired immunosuppressed critically ill Egyptian children. • The current study provides a new evidence that immune suppression can be measured and opens the way for further studies. • We mainly found that TNFα and BCL2 can predict HAI and MODS on admission but they were of no use in the follow-up. • Lymphocytic count, a rapid, easy and cheap test, can assess the immune state with a good predictive and follow up values.

Highlights • High-Resolution 3D radial Dixon MRI allows for the creation of quantitative fat fraction images. • Lymph node fat fractions improves diagnostic performance of MRI to detect axillary lymph node metastases. • Lymph node fat fractions are a promising quantitative indicator of metastases in axillary lymph nodes., Purpose To assess diagnostic performance of fat fractions (FF) from high-resolution 3D radial Dixon MRI for differentiating metastatic and non-metastatic axillary lymph nodes in breast cancer patients. Method High-resolution 3D radial Dixon MRI was prospectively performed on 1.5 T in 70 biopsy-verified breast cancer patients. 35 patients were available for analysis with histopathologic and imaging data. FF images were calculated as fat / in-phase. Two radiologists measured lymph node FF and assessed morphological features in one ipsilateral and one contralateral lymph node in consensus. Diagnostic performance of lymph node FF and morphological criteria were compared using histopathology as reference. Results 22 patients had metastatic axillary lymph nodes. Mean lymph node FF were 0.20 ± 0.073, 0.31 ± 0.079, and 0.34 ± 0.15 (metastatic, non-metastatic ipsi- and non-metastatic contralateral lymph nodes, respectively). Metastatic lymph node FF were significantly lower than non-metastatic ipsi- (p

Obesity indicators are known to predict the presence of type 2 diabetes mellitus (T2DM); however, evidence for which indicator best identifies undiagnosed T2DM in the Indian population is still very limited. In the present study we examined the utility of different obesity indicators to identify the presence of undiagnosed T2DM and determined their appropriate cut point for each obesity measure. Individuals were recruited from the large-scale population-based Kerala Diabetes Prevention Program. Oral glucose tolerance tests was performed to diagnose T2DM. Receiver operating characteristic (ROC) curve analyses were used to compare the association of different obesity indicators with T2DM and to determine the optimal cut points for identifying T2DM. A total of 357 new cases of T2DM and 1352 individuals without diabetes were identified. The mean age of the study participants was 46⋅4 (sd 7⋅4) years and 62 % were men. Waist circumference (WC), waist:hip ratio (WHR), waist:height ratio (WHtR), BMI, body fat percentage and fat per square of height were found to be significantly higher (P < 0⋅001) among those with diabetes compared with individuals without diabetes. In addition, ROC for WHR (0⋅67; 95 % 0⋅59, 0⋅75), WHtR (0⋅66; 95 % 0⋅57, 0⋅75) and WC (0⋅64; 95 % 0⋅55, 0⋅73) were shown to better identify patients with T2DM. The proposed cut points with an optimal sensitivity and specificity for WHR, WHtR and WC were 0⋅96, 0⋅56 and 86 cm for men and 0⋅88, 0⋅54 and 83 cm for women, respectively. The present study has shown that WHR, WHtR and WC are better than other anthropometric measures for detecting T2DM in the Indian population. Their utility in clinical practice may better stratify at-risk patients in this population than BMI, which is widely used at present.

Background: The diagnosis of house dust mite (HDM) allergy based on Skin prick test (SPT) is not accurate, especially in lower risk cases. Our aim is to develop and validate a predictive model to diagnose the HDM allergic symptoms (urticaria, allergic rhinitis, asthma). Methods: A forward-step logistic regression model was developed using a data set of 537 patients of West Bengal, India consisting of clinical variables (SPT based on 6 allergens of house dust and house dust mites, total IgE) and demographic characteristics (age, sex, house conditions). The output probability was estimated from the allergic symptoms shown by the patients. We finally prospectively validated a data set of 600 patients. Results: The gradual inclusion of the variables increased the correlation between observed and predicted probabilities (correlation coefficient (r2) = 0.97). The model development using group-1 showed an accuracy rate of 99%, sensitivity and specificity of 99.7% and 88.6% respectively and the area under the receiver operating characteristics (ROC) curve (AUC) of 99%. The corresponding numbers for the validation of our model with group-2 were 87%, 95.6% and 66% and 86% respectively. The model predicted the probability of symptoms better than SPTs in combination (accuracy rate 0.76–0.80), especially in lower risk cases (probability< 0.8) that are highly difficult to diagnose. Conclusion: This is perhaps the first attempt to model the outcome of HDM allergy in terms of symptoms, which could open up an alternative but highly efficient way for accurate diagnosis of HDM allergy enhancing the efficiency of immunotherapy. Keywords: Asthma, House dust mite allergy, Logistic regression model, ROC, SPT

To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji Medical College, China. A total of 151 cases from Jan. 26 to Mar. 20, 2020, were included. Then we followed 5 steps to predict and evaluate the model: data preprocessing, data splitting, feature selection, model building, prevention of overfitting, and Evaluation, and combined with artificial neural network algorithms. We processed the results in the 5 steps. In feature selection, ALB showed a strong negative correlation (r = 0.771, P

Background Both type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) are common age-associated disorders and T2DM patients show an increased risk to suffer from AD, however, there is currently no marker to identify who in T2DM populations will develop AD. Since glycogen synthase kinase-3β (GSK-3β) activity, ApoE genotypes and olfactory function are involved in both T2DM and AD pathogenesis, we investigate whether alterations of these factors can identify cognitive impairment in T2DM patients. Methods The cognitive ability was evaluated using Minimum Mental State Examination (MMSE) and Clinical Dementia Rating (CDR), and the mild cognitive impairment (MCI) was diagnosed by Petersen's criteria. GSK-3β activity in platelet, ApoE genotypes in leucocytes and the olfactory function were detected by Western/dot blotting, the amplification refractory mutation system (ARMS) PCR and the Connecticut Chemosensory Clinical Research Center (CCCRC) test, respectively. The odds ratio (OR) and 95% confidence intervals (95% CI) of the biomarkers for MCI diagnosis were calculated by logistic regression. The diagnostic capability of the biomarkers was evaluated by receiver operating characteristics (ROC) analyses. Findings We recruited 694 T2DM patients from Jan. 2012 to May. 2015 in 5 hospitals (Wuhan), and 646 of them met the inclusion criteria and were included in this study. 345 patients in 2 hospitals were assigned to the training set, and 301 patients in another 3 hospitals assigned to the validation set. Patients in each set were randomly divided into two groups: T2DM without MCI (termed T2DM-nMCI) or with MCI (termed T2DM-MCI). There were no significant differences for sex, T2DM years, hypertension, hyperlipidemia, coronary disease, complications, insulin treatment, HbA1c, ApoE ε2, ApoE ε3, tGSK3β and pS9GSK3β between the two groups. Compared with the T2DM-nMCI group, T2DM-MCI group showed lower MMSE score with older age, ApoE ε4 allele, higher olfactory score and higher rGSK-3β (ratio of total GSK-3β to Ser9-phosphorylated GSK-3β) in the training set and the validation set. The OR values of age, ApoE ε4 gene, olfactory score and rGSK-3β were 1.09, 2.09, 1.51, 10.08 in the training set, and 1.06, 2.67, 1.47, 7.19 in the validation set, respectively. The diagnostic accuracy of age, ApoE ε4 gene, olfactory score and rGSK-3β were 0.76, 0.72, 0.66, 0.79 in the training set, and 0.70, 0.68, 0.73, 0.79 in the validation set, respectively. These four combined biomarkers had the area under the curve (AUC) of 82% and 86%, diagnostic accuracy of 83% and 81% in the training set and the validation set, respectively. Interpretation Aging, activation of peripheral circulating GSK-3β, expression of ApoE ε4 and increase of olfactory score are diagnostic for the mild cognitive impairment in T2DM patients, and combination of these biomarkers can improve the diagnostic accuracy., Highlights • ApoE ε4 gene, platelet GSK-3β activation, olfactory dysfunction and aging are non-invasive, affordable and accessible biomarkers for diagnosing mild cognitive impairment in type 2 diabetes mellitus patients, and the combination of these non-invasive, affordable and accessible biomarkers can improve the accuracy of the diagnosis. Epidemiological studies show that type 2 diabetes mellitus is an independent risk factor of Alzheimer disease, and a large proportion of diabetic patients will develop Alzheimer disease, but no early diagnostic tool to identify them. We find that ApoE ε4 gene, platelet GSK-3β activation, olfactory dysfunction and aging are early markers for dementia in type 2 diabetes patients, and combination of these non-invasive markers can improve the diagnostic accuracy. These findings shed light on the early identification in type 2 diabetes population who will develop Alzheimer disease and thus enable early intervention to this currently incurable neurodegenerative disorder.

The work described has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement No. 115565, the resources for which are composed of a financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) companies'in-kind contribution. We thank all the members of PRECISESADS Consortium and INSERM U1227 for their effort in the sample recruitment, distribution and assessment of the samples used in this study. We are grateful to Dr Wesley H Brooks (Tampa, USA) for editorial assistance and to Valerie Le Troadec for secretarial assistance., High amount of polyclonal free light chains (FLC) are reported in systemic autoimmune diseases (SAD) and we took advantage of the PRECISESADS study to better characterize them. Serum FLC levels were explored in 1979 patients with SAD (RA, SLE, SjS, Scl, APS, UCTD, MCTD) and 614 healthy controls. Information regarding clinical parameters, disease activity, medications, autoantibodies (Ab) and the interferon α and/or γ scores were recorded. Among SAD patients, 28.4% had raised total FLC (from 12% in RA to 30% in SLE and APS) with a normal kappa/ lambda ratio. Total FLC levels were significantly higher in SAD with inflammation, active disease in SLE and SjS, and an impaired pulmonary functional capacity in SSc, while independent from kidney impairment, infection, cancer and treatment. Total FLC concentrations were positively correlated among the 10/17 (58.8%) autoantibodies (Ab) tested with anti-RNA binding protein Ab (SSB, SSA-52/60 kDa, Sm, U1-RNP), anti-dsDNA/nucleosome Ab, rheumatoid factor and negatively correlated with complement fractions C3/C4. Finally, examination of interferon (IFN) expression as a potential driver of FLC overexpression was tested showing an elevated level of total FLC among patients with a high IFNα and IFNγ Kirou's score, a strong IFN modular score, and the detection in the sera of B-cell IFN dependent factors, such as TNF-R1/TNFRSF1A and CXCL10/IP10. In conclusion, an elevated level of FLC, in association with a strong IFN signature, defines a subgroup of SAD patients, including those without renal affectation, characterized by increased disease activity, autoreactivity, and complement reduction., Innovative Medicines Initiative Joint Undertaking 115565, European Commission FP7/2007-2013, European Federation of Pharmaceutical Industries and Associations (EFPIA), Institut National de la Sante et de la Recherche Medicale (Inserm), European Commission U1227

Highlights • Each unit percent of lung involved resulted in a 3.6 % increase in the chance of in-hospital mortality (OR 1.036, P = 0.007). • The quantitative lung consolidation was also associated with ocurrence of major adverse hospital events (OR 1.025. P = 0.002). • These associations between lung involvement and outcomes were statistically significant after adjusting for relevant clinical parameters., Purpose To determine whether the percentage of lung involvement at the initial chest computed tomography (CT) is related to the subsequent risk of in-hospital death in patients with coronavirus disease-2019 (Covid-19). Materials and methods Using a cohort of 154 laboratory-confirmed Covid-19 pneumonia cases that underwent chest CT between February and April 2020, we performed a volumetric analysis of the lung opacities. The impact of relative lung involvement on outcomes was evaluated using multivariate logistic regression. The primary endpoint was the in-hospital mortality rate. The secondary endpoint was major adverse hospitalization events (intensive care unit admission, use of mechanical ventilation, or death). Results The median age of the patients was 65 years: 50.6 % were male, and 36.4 % had a history of smoking. The median relative lung involvement was 28.8 % (interquartile range 9.5–50.3). The overall in-hospital mortality rate was 16.2 %. Thirty-six (26.3 %) patients were intubated. After adjusting for significant clinical factors, there was a 3.6 % increase in the chance of in-hospital mortality (OR 1.036; 95 % confidence interval, 1.010–1.063; P = 0.007) and a 2.5 % increase in major adverse hospital events (OR 1.025; 95 % confidence interval, 1.009–1.042; P = 0.002) per percentage unit of lung involvement. Advanced age (P = 0.013), DNR/DNI status at admission (P

Background Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005). Conclusions FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter., Highlights • MRI-based classification was performed on presymptomatic FTD gene carriers and controls. • Presymptomatic carriers were separated from controls significantly better than chance-level. • The best performing model was based on white matter features from structural and diffusion scans. • Multimodal MRI-based classification scores may be useful to aid earlier FTD diagnosis.

Background: Accurate forecast of the death risk is crucial to the administration of people living with HIV/AIDS (PLHIV). We aimed to establish and validate an effective prognosis nomogram in PLHIV receiving antiretroviral therapy (ART). Methods: All the data were obtained from 2006 to 2018 in the Wenzhou area from China AIDS prevention and control information system. Factors included in the nomogram were determined by univariate and multiple Cox proportional hazard analysis based on the training set. The receiver operating characteristic (ROC) and calibration curves were used to assess its predictive accuracy and discriminative ability. Its clinical utility was also evaluated using decision curve analysis (DCA), X-tile analysis and Kaplan-Meier curve, respectively in an independent validation set. Findings: Independent prognostic factors including haemoglobin, viral load and CD4+ T-cell count were determined and contained in the nomogram. Good agreement between the prediction by nomogram and actual observation could be detected in the calibration curve for mortality, especially in the first year. In the training cohort, AUC (95% CI) and C-index (95% CI) were 0.93 (0.90, 0.96) and 0.90 (0.85, 0.96), respectively. In the validation set, the nomogram still revealed excellent discriminations [AUC (95% CI): 0.95 (0.91, 1.00)] and good calibration [C-index (95% CI): 0.92 (0.82–1.00)]. Moreover, DCA also demonstrated that the nomogram was clinical beneficial. Additionally, participants could be classified into three distinct (low, middle and high) risk groups by the nomogram. Interpretation: The nomogram presents accurate and favourable prognostic prediction for PLHIV who underwent ART. Funding: This work was supported by Zhejiang Basic Public Welfare Research Project (LGF19H260011), Wenzhou Basic Public Welfare Research Project (Y20180201), the Initial Scientific Research Fund (KYQD170301), the Major Project of the Eye Hospital Wenzhou the Major Project of the Eye Hospital Wenzhou Medical University (YNZD201602). Part of this work was also funded by National Natural Science Foundation of China (81670777) and Science and Technology Innovation Activity Plan and New Talents Plan for College Students in Zhejiang Province (2019R413073). The funders had no roles in study design, data collection, data analysis, interpretation and writing of the report. Keywords: PLHIV, Nomogram, Prognosis, HIV/AIDS-related mortality, DCA

Background Advanced brain MR techniques are useful tools for differentiating Progressive Supranuclear Palsy from Parkinson's disease, although time-consuming and unlikely to be used all together in routine clinical work. We aimed to compare the diagnostic accuracy of quantitative morphometric, volumetric and DTI metrics for differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease. Methods 23 Progressive Supranuclear Palsy-Richardson's Syndrome and 42 Parkinson's disease patients underwent a standardized 1.5T brain MR protocol comprising high-resolution T1W1 and DTI sequences. Brainstem and cerebellar peduncles morphometry, automated volumetric analysis of brain deep gray matter and DTI metric analyses of specific brain structures were carried out. We determined diagnostic accuracy, sensitivity and specificity of MR-markers with respect to the clinical diagnosis by using univariate receiver operating characteristics curve analyses. Age-adjusted multivariate receiver operating characteristics analyses were then conducted including only MR-markers with a sensitivity and specificity exceeding 80%. Results Morphometric markers (midbrain area, pons to midbrain area ratio and MR Parkinsonism Index), DTI parameters (infratentorial structures) and volumetric analysis (thalamus, putamen and pallidus nuclei) presented moderate to high diagnostic accuracy in discriminating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, with midbrain area showing the highest diagnostic accuracy (99%) (mean ± standard deviation: 75.87 ± 16.95 mm2vs 132.45 ± 20.94 mm2, respectively; p, Highlights • We compared quantitative brain MR markers accuracy to differentiate advanced stages of PSP-RS from PD. • Quantitative morphometric, DTI and volumetric data showed moderate-high accuracies. • Midbrain area alone best discriminated advanced PSP-RS from PD (99%). • Midbrain area evaluation should be added to brain MR protocols for parkinsonisms.