Radiomics and artificial intelligence for precision medicine in lung cancer treatment.

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC).

BACKGROUND: Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality. METHODS: RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models. RESULTS: The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06-2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56-2.62; P = 0.00647). CONCLUSIONS: RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.

Development and Experimental Research of VFTO Measuring Sensor.

Very fast transient overvoltage (VFTO) generated by an operating disconnector is one of the main reasons for electromagnetic disturbance in gas-insulated switchgear (GIS) substations. Generally, the amplitude of VFTO can be used as one of the references for the insulation design of GIS primary electric power equipment, so it is necessary to obtain its accurate amplitude. In this study, a new VFTO measuring sensor is developed and its measurement performance is demonstrated through hundreds of operations by a disconnector in a 220 kV GIS test circuit. The validation shows that the low cut-off frequency of the new VFTO measuring sensor has been greatly expanded to 0.01 mHz, which is improved by about 50% compared with the old sensor. The measurement accuracy of amplitude of VFTO micro-pulse improves greatly by about 80% compared with the old one. Thus, the new VFTO measuring sensor can fully meet the measurement needs of trapped charge voltage, power frequency voltage, and high-frequency transient voltage in VFTO waveform. It can be used to provide more accurate data support for insulation design of GIS primary power electric equipment in extra-high voltage (EHV) and ultra-high voltage (UHV) GIS substations.

Discovery of a biomarker candidate for surgical stratification in high-grade serous ovarian cancer.

BACKGROUND: Maximal effort cytoreductive surgery is associated with improved outcomes in advanced high-grade serous ovarian cancer (HGSOC). However, despite complete gross resection (CGR), there is a percentage of patients who will relapse and die early. The aim of this study is to identify potential candidate biomarkers to help personalise surgical radicality. METHODS: 136 advanced HGSOC cases who underwent CGR were identified from three public transcriptomic datasets. Candidate prognostic biomarkers were discovered in this cohort by Cox regression analysis, and further validated by targeted RNA-sequencing in HGSOC cases from Imperial College Healthcare NHS Trust (n = 59), and a public dataset. Gene set enrichment analysis was performed to understand the biological significance of the candidate biomarker. RESULTS: We identified ALG5 as a prognostic biomarker for early tumour progression in advanced HGSOC despite CGR (HR = 2.42, 95% CI (1.57-3.75), p < 0.0001). The prognostic value of this new candidate biomarker was additionally confirmed in two independent datasets (HR = 1.60, 95% CI (1.03-2.49), p = 0.0368; HR = 3.08, 95% CI (1.07-8.81), p = 0.0365). Mechanistically, the oxidative phosphorylation was demonstrated as a potential biological pathway of ALG5-high expression in patients with early relapse (p < 0.001). CONCLUSION: ALG5 has been identified as an independent prognostic biomarker for poor prognosis in advanced HGSOC patients despite CGR. This sets a promising platform for biomarker combinations and further validations towards future personalised surgical care.

Evaluation of work resumption strategies after COVID-19 reopening in the Chinese city of Shenzhen: a mathematical modeling study.

OBJECTIVES: As China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. STUDY DESIGN: Based on the classic SEITR model and combined with population mobility, a compartmental model was constructed to simulate the transmission of COVID-19 and disease progression in the Shenzhen population. METHODS: Based on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios. RESULTS: Imported cases account for most (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage after work resumption, various resumptions resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption. CONCLUSION: Regardless of the work resumption strategy adopted in Shenzhen, the risk of a resurgence of COVID-19 after its reopening was limited. The strict control of imported cases and extensive use of facial masks play a key role in COVID-19 prevention.

Radiological assessment of Peritoneal Cancer Index on preoperative CT in ovarian cancer is related to surgical outcome and survival.

PURPOSE: To evaluate whether Peritoneal Cancer Index (PCI) assessed on preoperative CT (CT-PCI) can be used as non-invasive preoperative tool to predict surgical outcome, disease-free survival (DFS) and overall survival (OS). MATERIALS AND METHODS: This is a retrospective, observational cohort study performed in a single institution. We considered all patients with diagnosis of ovarian cancer and preoperative CT, who had undergone upfront cytoreductive surgery between 2008 and 2010 and had post-operative clinical follow-up to December 2015. Two radiologists reviewed CT scans and assessed CT-PCI using Sugarbaker's diagram. We assessed the discriminatory capacity of the CT-PCI score on the surgical outcome by ROC curve analysis. DFS and OS were assessed by Kaplan-Meier nonparametric curves and by multivariable Cox-regression analysis. RESULTS: A total of 297 patients were included in the present analysis. CT-PCI was positively correlated with post-operative residual disease [odds ratio (OR) 1.04, 95% CI 1.01-1.07, p = 0.003]. ROC curve analysis returned AUC = 0.64 for the prediction of total macroscopic tumour clearance. In multivariable analysis, patients with no peritoneal disease seen on CT had a significantly longer DFS [Hazard ratio (HR) 2.28, p = 0.007]. Radiological serosal small bowel involvement was an independent predictor for shorter OS (HR 3.01, p = 0.002). CONCLUSION: Radiological PCI assessed on preoperative CT is associated with the probability of residual disease after cytoreductive surgery; however, it has low performance as a triage test to reliably identify patients who are likely to have complete cytoreductive surgery. CT-PCI is positively correlated with both DFS and OS and may be used as an independent prognostic factor, for example in patients with high FIGO stages.

Discovery of pre-therapy 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients.

PURPOSE: The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: Pre-therapy PET scans from a total of 358 Stage I-III NSCLC patients scheduled for radiotherapy/chemo-radiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. A semi-automatic threshold method was used to segment the primary tumors. Radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis was used for data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. RESULTS: Of 358 patients, 249 died within the follow-up period [median 22 (range 0-85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16-2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. CONCLUSION: PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy.

The RNA-binding protein LARP1 is a post-transcriptional regulator of survival and tumorigenesis in ovarian cancer.

RNA-binding proteins (RBPs) are increasingly identified as post-transcriptional drivers of cancer progression. The RBP LARP1 is an mRNA stability regulator, and elevated expression of the protein in hepatocellular and lung cancers is correlated with adverse prognosis. LARP1 associates with an mRNA interactome that is enriched for oncogenic transcripts. Here we explore the role of LARP1 in epithelial ovarian cancer, a disease characterized by the rapid acquisition of resistance to chemotherapy through the induction of pro-survival signalling. We show, using ovarian cell lines and xenografts, that LARP1 is required for cancer cell survival and chemotherapy resistance. LARP1 promotes tumour formation in vivo and maintains cancer stem cell-like populations. Using transcriptomic analysis following LARP1 knockdown, cross-referenced against the LARP1 interactome, we identify BCL2 and BIK as LARP1 mRNA targets. We demonstrate that, through an interaction with the 3' untranslated regions (3' UTRs) of BCL2 and BIK, LARP1 stabilizes BCL2 but destabilizes BIK with the net effect of resisting apoptosis. Together, our data indicate that by differentially regulating the stability of a selection of mRNAs, LARP1 promotes ovarian cancer progression and chemotherapy resistance.

Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360° Image Outpainting.

360° images, with a field-of-view (FoV) of $180^{\circ}\times 360^{\circ}$, provide immersive and realistic environments for emerging virtual reality (VR) applications, such as virtual tourism, where users desire to create diverse panoramic scenes from a narrow FoV photo they take from a viewpoint via portable devices. It thus brings us to a technical challenge: 'How to allow the users to freely create diverse and immersive virtual scenes from a narrow FoV image with a specified viewport?' To this end, we propose a transformer-based 360° image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360° images. Compared with existing methods, e.g., [3], which primarily focus on inputs with rectangular masks and central locations while overlooking the spherical property of 360° images, our Dream360 offers higher outpainting flexibility and fidelity based on the spherical representation. Dream360 comprises two key learning stages: (I) codebook-based panorama outpainting via Spherical-VQGAN (S-VQGAN), and (II) frequency-aware refinement with a novel frequency-aware consistency loss. Specifically, S-VQGAN learns a sphere-specific codebook from spherical harmonic (SH) values, providing a better representation of spherical data distribution for scene modeling. The frequency-aware refinement matches the resolution and further improves the semantic consistency and visual fidelity of the generated results. Our Dream360 achieves significantly lower Frechet Inception Distance (FID) scores and better visual fidelity than existing methods. We also conducted a user study involving 15 participants to interactively evaluate the quality of the generated results in VR, demonstrating the flexibility and superiority of our Dream360 framework.

Amplified therapeutic targets in high-grade serous ovarian carcinoma - a review of the literature with quantitative appraisal.

High-grade serous ovarian carcinoma is a unique cancer characterised by universal TP53 mutations and widespread copy number alterations. These copy number alterations include deletion of tumour suppressors and amplification of driver oncogenes. Given their key oncogenic roles, amplified driver genes are often proposed as therapeutic targets. For example, development of anti-HER2 agents has been clinically successful in treatment of ERBB2-amplified tumours. A wide scope of preclinical work has since investigated numerous amplified genes as potential therapeutic targets in high-grade serous ovarian carcinoma. However, variable experimental procedures (e.g., choice of cell lines), ambiguous phenotypes or lack of validation hinders further clinical translation of many targets. In this review, we collate the genes proposed to be amplified therapeutic targets in high-grade serous ovarian carcinoma, and quantitatively appraise the evidence in support of each candidate gene. Forty-four genes are found to have evidence as amplified therapeutic targets; the five highest scoring genes are CCNE1, PAX8, URI1, PRKCI and FAL1. This review generates an up-to-date list of amplified therapeutic target candidates for further development and proposes comprehensive criteria to assist amplified therapeutic target discovery in the future.

High-efficiency degradation catalytic performance of a novel Angelica sinensis polysaccharide-silver nanomaterial for dyes by ultrasonic cavitation.

Currently, the polluted wastewater discharged by industry accounts for the major part of polluted bodies of water. As one of the industrial wastewaters, dye wastewater is characterized by high toxicity, wide pollution, and difficulty in decolorization degradation. In this paper, a novel composite nanomaterial catalyst of silver was prepared by using Angelica sinensis polysaccharide (ASP) as a reducing and stabilizing agent. And the optimum reaction conditions explored are VAgNO3 = 5 mL (300 mM) and vASP = 7% (w/v) for 6 h at 90 °C. In addition, the ASP-Ag nanocatalyst was characterized by several techniques. The results demonstrated that ASP-Ag nanoparticles were successfully synthesized. Degradation rate, which provides a numerical visualization of the percentage reduction in pollutant concentration. With the wrapping of ASP, the ultrasonic catalytic degradation rates of different organic dyes including rhodamine B (RB), methylene blue (MB), and methyl orange (MO) were from 88.2%, 88.7%, and 85.2% to 96.1%, 95.2% and 93.5% at room temperature, respectively. After the experiments, when cdyes = 10 mg/L, the highest degradation rate can be observed under cAPS-AgNPs = 10 mg/L with the most powerful cavitation frequency f = 59 kHz. The effect of ultrasonic frequency on the acoustic pressure distribution in the reactor was investigated by using COMSOL Multiphysis@ software to propose the mechanism of ultrasonic degradation and the mechanism was confirmed by OH radical trapping experiments. It indicates that OH produced by the ultrasonic cavitation effect plays a determinant role in the degradation. And then, the intermediate products of the dye degradation process were analyzed by gas chromatography and mass spectrometry (GC-MS), and the possible degradation processes of dyes were proposed. The resulting products of degradation are SO42-, NH4+, NO3-, N2, CO2 and H2O. Finally, the recycling degradation experiments showed that catalyst maintains a high degradation rate within reusing 5 cycles. Thus, this catalyst is highly efficient and recyclable.

A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC.

INTRODUCTION: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy. METHODS: This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively. RESULTS: LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 and hazard ratio = 2.45, 95% CI: 1.07-5.65, p = 0.035). CONCLUSIONS: A CT radiomics-based signature developed from response vector CD274 can aid in evaluating patients' suitability for PD-1 or PD-L1 checkpoint inhibitor immunotherapy in NSCLC.

Discriminatory Changes in Circulating Metabolites as a Predictor of Hepatocellular Cancer in Patients with Metabolic (Dysfunction) Associated Fatty Liver Disease.

Introduction: The burden of metabolic (dysfunction) associated fatty liver disease (MAFLD) is rising mirrored by an increase in hepatocellular cancer (HCC). MAFLD and its sequelae are characterized by perturbations in lipid handling, inflammation, and mitochondrial damage. The profile of circulating lipid and small molecule metabolites with the development of HCC is poorly characterized in MAFLD and could be used in future studies as a biomarker for HCC. Methods: We assessed the profile of 273 lipid and small molecule metabolites by ultra-performance liquid chromatography coupled to high-resolution mass spectrometry in serum from patients with MAFLD (n = 113) and MAFLD-associated HCC (n = 144) from six different centers. Regression models were used to identify a predictive model of HCC. Results: Twenty lipid species and one metabolite, reflecting changes in mitochondrial function and sphingolipid metabolism, were associated with the presence of cancer on a background of MAFLD with high accuracy (AUC 0.789, 95% CI: 0.721-0.858), which was enhanced with the addition of cirrhosis to the model (AUC 0.855, 95% CI: 0.793-0.917). In particular, the presence of these metabolites was associated with cirrhosis in the MAFLD subgroup (p < 0.001). When considering the HCC cohort alone, the metabolic signature was an independent predictor of overall survival (HR 1.42, 95% CI: 1.09-1.83, p < 0.01). Conclusion: These exploratory findings reveal a metabolic signature in serum which is capable of accurately detecting the presence of HCC on a background of MAFLD. This unique serum signature will be taken forward for further investigation of diagnostic performance as biomarker of early stage HCC in patients with MAFLD in the future.

Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer.

Tertiary lymphoid structure (TLS) is associated with prognosis in copy-number-driven tumors, including high-grade serous ovarian cancer (HGSOC), although the function of TLS and its interaction with copy-number alterations in HGSOC are not fully understood. In the current study, we confirm that TLS-high HGSOC patients show significantly better progression-free survival (PFS). We show that the presence of TLS in HGSOC tumors is associated with B cell maturation and cytotoxic tumor-specific T cell activation and proliferation. In addition, the copy-number loss of IL15 and CXCL10 may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed. Last, a radiomics-based signature is developed to predict the presence of TLS, which independently predicts PFS in both HGSOC patients and immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients. Overall, we reveal that TLS coordinates intratumoral B cell and T cell response to HGSOC tumor, while the cancer genome evolves to counteract TLS formation and function.

Horizontal comparison of "red or blue shift" and binding energy of six fluoroquinolones: Fluorescence quenching mechanism, theoretical calculation and molecular modeling method.

In this article, the interaction between six fluoroquinolones (FQs) and bovine serum albumin (BSA) was initially studied at 298 K, 303 K and 310 K respectively under simulated physiological conditions by fluorescence spectroscopy. At the same time, the sub-structural domains on BSA that may bind to FQs were investigated by molecular docking simulation technique. A combination of quantitative and qualitative approaches was used in the analysis of the binding constants, binding sites and corresponding thermodynamic parameters in the interaction system, it was found that FQs forms a complex with BSA and undergoes static quenching, which is the main cause of fluorescence quenching. The results indicated that hydrogen bonds, Van der Waals force and electrostatic interaction were the main binding forces between the complexes, it also showed that these six fluoroquinolones mainly bound to the IIA and IIIA structural domains of BSA, while DANO and SARA may be more toxic than other antibiotics. Based on Foster's non-radiative energy transfer theory, the binding distance between FQs and BSA was calculated to be less than 7 nm, indicating the existence of energy transfer between small molecule drugs and proteins. Synchronous fluorescence and UV-Vis absorption spectroscopy further confirmed that FQs can alter the secondary conformational change of BSA. Lomefloxacin has a different effect from the other five fluoroquinolone antibiotics because it causes a decrease in polarity and an increase in hydrophobicity around tryptophan residues, while the other five FQs have the opposite effect. Together, the study of FQs-BSA is of great significance to elucidate the pharmacokinetics and pharmacodynamics of FQs.

Impacts of combined exposure to formaldehyde and PM2.5 at ambient concentrations on airway inflammation in mice.

Asthma is a respiratory disease that can be exacerbated by certain environmental factors. Both formaldehyde (FA) and PM2.5, the most common indoor and outdoor air pollutants in mainland China, are closely associated with the onset and development of asthma. To date, however, there is very little report available on whether there is an exacerbating effect of combined exposure to FA and PM2.5 at ambient concentrations. In this study, asthmatic mice were exposed to 1 mg/m3 FA, 1 mg/kg PM2.5, or a combination of 0.5 mg/m3 FA and 0.5 mg/kg PM2.5, respectively. Results demonstrated that both levels of oxidative stress and inflammation were significantly increased, accompanied by an obvious decline in lung function. Further, the initial activation of p38 MAPK and NF-κB that intensified the immune imbalance of asthmatic mice were found to be visibly mitigated following the administration of SB203580, a p38 MAPK inhibitor. Noteworthily, it was found that combined exposure to the two at ambient concentrations could significantly worsen asthma than exposure to each of the two alone at twice the ambient concentration. This suggests that combined exposure to formaldehyde and PM2.5 at ambient concentrations may have a synergistic effect, thus causing more severe damage in asthmatic mice. In general, this work has revealed that the combined exposure to FA and PM2.5 at ambient concentrations can synergistically aggravate asthma via the p38 MAPK pathway in mice.

A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease.

Background: Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods: We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results: The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions: This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.

Alzheimer's disease is the most common cause of dementia, impacting memory, thinking and behaviour. It can be challenging to diagnose Alzheimer's disease which can lead to suboptimal patient care. During the development of Alzheimer's disease the brain shrinks and the cells within it die. One method that can be used to assess brain function is magnetic resonance imaging, which uses magnetic fields and radio waves to produce images of the brain. In this study, we develop a method that uses magnetic resonance imaging data to identify differences in the brain between people with and without Alzheimer's disease, including before obvious shrinkage of the brain occurs. This method could be used to help diagnose patients with Alzheimer's Disease.

Novel Non-Congeneric Derivatives of the Choline Kinase Alpha Inhibitor ICL-CCIC-0019.

Choline kinase alpha (CHKA) is a promising target for the development of cancer therapeutics. We have previously reported ICL-CCIC-0019, a potent CHKA inhibitor with high cellular activity but with some unfavorable pharmacological properties. In this work, we present an active analogue of ICL-CCIC-0019 bearing a piperazine handle (CK146) to facilitate further structural elaboration of the pharmacophore and thus improve the biological profile. Two different strategies were evaluated in this study: (1) a prodrug approach whereby selective CHKA inhibition could be achieved through modulating the activity of CK146, via the incorporation of an ε-(Ac) Lys motif, cleavable by elevated levels of histone deacetylase (HDAC) and cathepsin L (CTSL) in tumour cells; (2) a prostate-specific membrane antigen (PSMA) receptor targeted delivery strategy. Prodrug (CK145) and PSMA-targeted (CK147) derivatives were successfully synthesized and evaluated in vitro. While the exploitation of CK146 in those two strategies did not deliver the expected results, important and informative structure-activity relationships were observed and have been reported.

The epidemiology, pathophysiological mechanisms, and management toward COVID-19 patients with Type 2 diabetes: A systematic review.

This review comprehensively summarizes epidemiologic evidence of COVID-19 in patients with Type 2 diabetes, explores pathophysiological mechanisms, and integrates recommendations and guidelines for patient management. We found that diabetes was a risk factor for diagnosed infection and poor prognosis of COVID-19. Patients with diabetes may be more susceptible to adverse outcomes associated with SARS-CoV-2 infection due to impaired immune function and possible upregulation of enzymes that mediate viral invasion. The chronic inflammation caused by diabetes, coupled with the acute inflammatory reaction caused by SARS-CoV-2, results in a propensity for inflammatory storm. Patients with diabetes should be aware of their increased risk for COVID-19.

Transcriptional analysis of multiple ovarian cancer cohorts reveals prognostic and immunomodulatory consequences of ERV expression.

BACKGROUND: Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC. METHODS: ERV expression was assessed using EOC transcriptional data from The Cancer Genome Atlas (TCGA) and from an independent cohort (Hammersmith Hospital, HH), as well as from untreated or DNMTi-treated EOC cell lines. Least absolute shrinkage and selection operator (LASSO) logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells. RESULTS: ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+T cells (r=0.46, p=0.0001). In the TCGA dataset, a higher ERV score was found in BRCA1/2 mutant tumors, compared to wild type (p=0.015), while a lower ERV score was found in CCNE1 amplified tumors, compared to wild type (p=0.019). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of an ERV expression threshold by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells. CONCLUSIONS: These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence tumor immune infiltration and affect antitumor immunity.