The BRCA1 BRCT promotes antisense RNA production and double-stranded RNA formation to suppress ribosomal R-loops.

R-loops, or RNA:DNA hybrids, can induce DNA damage, which requires DNA repair factors including breast cancer type 1 susceptibility protein (BRCA1) to restore genomic integrity. To date, several pathogenic mutations have been found within the tandem BRCA1 carboxyl-terminal (BRCT) domains that mediate BRCA1 interactions with proteins and DNA in response to DNA damage. Here, we describe a nonrepair role of BRCA1 BRCT in suppressing ribosomal R-loops via two mechanisms. Through its RNA binding and annealing activities, BRCA1 BRCT facilitates the formation of double-stranded RNA between ribosomal RNA (rRNA) and antisense-rRNA (as-rRNA), hereby minimizing rRNA hybridization to ribosomal DNA to form R-loops. BRCA1 BRCT also promotes RNA polymerase I-dependent transcription of as-rRNA to enhance double-stranded rRNA (ds-rRNA) formation. In addition, BRCA1 BRCT-mediated as-rRNA production restricts rRNA maturation in unperturbed cells. Hence, impairing as-rRNA transcription and ds-rRNA formation due to BRCA1 BRCT deficiency deregulates rRNA processing and increases ribosomal R-loops and DNA breaks. Our results link ribosomal biogenesis dysfunction to BRCA1-associated genomic instability.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Utility of Bedside Ultrasound in Percutaneous Tracheostomy.

Background: Percutaneous tracheostomy placement is a common procedure performed in the intensive care unit. The use of an anterior neck ultrasound exam is routinely performed preprocedure, allowing for vessel visualization in determining the safety and feasibility of performing the procedure bedside. This prospective observational cohort study was conducted to determine whether vasculature in the anterior neck, seen on bedside ultrasound exam, contributes to bleeding complications during or after percutaneous tracheostomy (PCT) placement. Research Question: Do the vessels identified on preprocedure neck ultrasound affect the risk of bleeding during and after bedside PCT placement? Study Design and Methods: Preprocedural ultrasound was used to identify standard anatomical landmarks and vascular structures in the anterior neck in all patients undergoing bedside PCT placement under bronchoscopic guidance. A blinded survey of our recorded preprocedural images was provided to an expert panel who regularly perform bedside PCTs to determine the influence the images have on their decision to perform the procedure at the bedside. Results: One out of 15 patients (7%) had intra-operative minimal bleeding which was not clinically significant and resolved by gauze compression for 30 s. None of the patients had post-procedural bleeding after tracheostomy placement. Based on the blinded interpretation of neck ultrasound, there was 0.214 inter-operator variability among the expert panelists for decision-making regarding performing bedside PCT. Interpretation: Vessels visualized with anterior neck ultrasound were found to be small venous structures and did not significantly contribute to bleeding risk in patients who underwent PCT placement. The size and location of veins on neck ultrasound may commonly contribute to abandoning bedside PCT. This study suggests that veins measuring 3.9 mm or smaller identified at the site of access do not increase the risk of bleeding in PCT placement.

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis.

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

Rapid quantification of alcohol production in microorganisms based on nanostructure-initiator mass spectrometry (NIMS).

We described a mass spectrometry-based assay to rapidly quantify the production of primary alcohols directly from cell cultures. This novel assay used the combination of TEMPO-based oxidation chemistry and oxime ligation, followed by product analysis based on Nanostructure-Initiator Mass Spectrometry. This assay enables quantitative monitor both C5 to C18 alcohols as well as glucose and gluconate in the growth medium to support strain characterization and optimization. We find that this assay yields similar results to gas chromatography for isoprenol production but required much less acquisition time per sample. We applied this assay to gain new insights into P. Putida's utilization of alcohols and find that this strain largely could not grow on heptanol and octanol.

Development of engineered Candida tropicalis strain for efficient corncob-based xylitol-ethanol biorefinery.

BACKGROUND: Xylitol has a wide range of applications in the pharmaceuticals, cosmetic, food and beverage industry. Microbial xylitol production reduces the risk of contamination and is considered as environment friendly and sustainable compared to the chemical method. In this study, random mutagenesis and genetic engineering approaches were employed to develop Candida tropicalis strains with reduced xylitol dehydrogenase (XDH) activity to eliminate co-substrate requirement for corn cob-based xylitol-ethanol biorefinery. RESULTS: The results suggest that when pure xylose (10% w/v) was fermented in bioreactor, the Ethyl methane sulfonate (EMS) mutated strain (C. tropicalis K2M) showed 9.2% and XYL2 heterozygous (XYL2/xyl2Δ::FRT) strain (C. tropicalis K21D) showed 16% improvement in xylitol production compared to parental strain (C. tropicalis K2). Furthermore, 1.5-fold improvement (88.62 g/L to 132 g/L) in xylitol production was achieved by C. tropicalis K21D after Response Surface Methodology (RSM) and one factor at a time (OFAT) applied for media component optimization. Finally, corncob hydrolysate was tested for xylitol production in biorefinery mode, which leads to the production of 32.6 g/L xylitol from hemicellulosic fraction, 32.0 g/L ethanol from cellulosic fraction and 13.0 g/L animal feed. CONCLUSIONS: This work, for the first time, illustrates the potential of C. tropicalis K21D as a microbial cell factory for efficient production of xylitol and ethanol via an integrated biorefinery framework by utilising lignocellulosic biomass with minimum waste generation.

Selective targeting of TET catalytic domain promotes somatic cell reprogramming.

Ten-eleven translocation (TET) family enzymes (TET1, TET2, and TET3) oxidize 5-methylcytosine (5mC) and generate 5-hydroxymethylcytosine (5hmC) marks on the genome. Each TET protein also interacts with specific binding partners and partly plays their role independent of catalytic activity. Although the basic role of TET enzymes is well established now, the molecular mechanism and specific contribution of their catalytic and noncatalytic domains remain elusive. Here, by combining in silico and biochemical screening strategy, we have identified a small molecule compound, C35, as a first-in-class TET inhibitor that specifically blocks their catalytic activities. Using this inhibitor, we explored the enzymatic function of TET proteins during somatic cell reprogramming. Interestingly, we found that C35-mediated TET inactivation increased the efficiency of somatic cell programming without affecting TET complexes. Using high-throughput mRNA sequencing, we found that by targeting 5hmC repressive marks in the promoter regions, C35-mediated TET inhibition activates the transcription of the BMP-SMAD-ID signaling pathway, which may be responsible for promoting somatic cell reprogramming. These results suggest that C35 is an important tool for inducing somatic cell reprogramming, as well as for dissecting the other biological functions of TET enzymatic activities without affecting their other nonenzymatic roles.

Factors affecting clinical outcome in COVID-associated rhino-orbito-cerebral mucormycosis (CAROM) patients-An ambispective, single-arm, observational study.

BACKGROUND: COVID-associated Rhino-Orbito-Cerebral Mucormycosis (CAROM) appeared as an epidemic in India during the second wave of the COVID-19 pandemic during the months of March to May 2021. Though many reports have highlighted cross sectional and short-term attributes related to CAROM, long term follow up data is sparse. OBJECTIVE: This report aims to analyze the follow-up outcomes in consecutive patients presenting to us during the epidemic. PATIENTS AND METHODS: This was an ambispective observational analytical study, recruiting the consecutive patients admitted to our tertiary care centre during the period of the CAROM epidemic. The mortality rate during the follow-up and various factors affecting survival were studied using univariable and multivariable statistics with the Stata 14.0 software. RESULTS: Of the 189 patients studied, eight were lost to follow-up. The outcome analysis was performed for the 181 patients. 93.6 % (162/173) of the patients had diabetes. The All-cause mortality was 45 % (81/181), while the ROCM-specific mortality was found to be 24 % (46/181) at a median follow-up of 176 days (IQR: 21-217 days). With univariable analysis, increasing age, higher serum IL-6 levels, presence of additional comorbidities (in addition to Diabetes and hypertension), bilateral disease, skin necrosis, palatal involvement, infratemporal fossa involvement, and impaired vision/ocular movements were found to be associated with increased mortality. However, on multivariable analysis, only 1) increasing age, 2) raised serum IL-6 levels, and 3) bilateral disease were predictive of increased mortality. Surgical debridement (endoscopic, palatal removal, orbital exenteration, neurosurgical intervention) was associated with significantly reduced mortality on both univariable and multivariable analysis. CONCLUSION: Our intermediate-term follow-up data showed advanced age at presentation, raised IL-6 levels, and bilateral sinonasal involvement to be predictive of increased mortality, while surgical debridement is significantly protective from mortality in CAROM patients.

Surgical Challenges in the Management of Post COVID-19 Midface Mucormycosis (PCoMM): An Institutional Protocol.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed another serious threat, mucormycosis infection, affecting the maxilla and orbitocerebral region. This condition has not spared world population from its merciless claws. This article addresses the challenges faced by the maxillofacial surgeons in setting the protocols from preoperative diagnosis, surgical management to postoperative care, including short-term and long-term rehabilitation. To manage this relentlessly progressing condition, a multispecialty team approach is to be activated in diagnosing, managing, and rehabilitating the patients. PURPOSE: The purpose of this clinical study is to document and analyze the clinical and demographic data, presentation of the lesion, the diagnostic methods followed for early clinical detection, and management of post COVID-19 midface mucormycosis. The article also discusses postoperative medical management and prosthetic rehabilitation. RESULTS: Most of the mucormycosis cases reporting to our center were treated and recovered patients of Severe Acute Respiratory Syndrome Coronavirus 2 infection. Thirty-four (n=34) case were operated for post COVID-19 midface mucormycosis between October 2020 and December 2021. Male to Female ratio is 1:42. The average age of the patients was 57.5 years. Maximum patients were in fifth and sixth decade of life. Maxilla was the involved bone. Treatment was primarily surgical debridement to extended or radical maxillectomy. All patients were treated with Liposomal Amphotericin B and tab posaconazole for 3 to 4 weeks depending upon the age, weight, and physiological state of the patients to attain an optimal cumulative load. Three patients succumbed to illness postoperatively (n=3, 1.02%). Average duration of hospital stay was 47 days. The average review period was 5.1 months.

Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features.

Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp-MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp-MRI features for the characterization of breast tumors (malignant vs. benign and low- vs. high-grade). This study included the breast mp-MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp-MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10-fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low- versus high-grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors.

Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging.

Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.

Device for Assessing Knee Joint Dynamics During Magnetic Resonance Imaging.

BACKGROUND: Knee assessment with and without load using magnetic resonance imaging (MRI) can provide information on knee joint dynamics and improve the diagnosis of knee joint diseases. Performing such studies on a routine MRI-scanner require a load-exerting device during scanning. There is a need for more studies on developing loading devices and evaluating their clinical potential. PURPOSE: Design and develop a portable and easy-to-use axial loading device to evaluate the knee joint dynamics during the MRI study. STUDY TYPE: Prospective study. SUBJECTS: Nine healthy subjects. FIELD STRENGTH/SEQUENCE: A 0.25 T standing-open MRI and 3.0 T MRI. PD-T2 -weighted FSE, 3D-fast-spoiled-gradient-echo, FS-PD, and CartiGram sequences. ASSESSMENT: Design and development of loading device, calibration of loads, MR safety assessment (using projectile angular displacement, torque, and temperature tests). Scoring system for ease of doing. Qualitative (by radiologist) and quantitative (using structural similarity index measure [SSIM]) image-artifact assessment. Evaluation of repeatability, comparison with various standing stances load, and loading effect on knee MR parameters (tibiofemoral bone gap [TFBG], femoral cartilage thickness [FCT], tibial cartilage thickness [TCT], femoral cartilage T2 -value [FCT2], and tibia cartilage T2 -value [TCT2]). The relative percentage change (RPC) in parameters due to the device load was computed. STATISTICAL TEST: Pearson's correlation coefficient (r). RESULTS: The developed device is conditional-MR safe (details in the manuscript and supplementary materials), 15 × 15 × 45 cm3 dimension, and <3 kg. The ease of using the device was 4.9/5. The device introduced no visible image artifacts, and SSIM of 0.9889 ± 0.0153 was observed. The TFBG intraobserver variability (absolute difference) was <0.1 mm. Interobserver variability of all regions of interest was <0.1 mm. The load exerted by the device was close to the load during standing on both legs in 0.25 T scanner with r > 0.9. Loading resulted in RPC of 1.5%-11.0%, 7.9%-8.5%, and -1.5% to 13.0% in the TFBG, FCT, and TCT, respectively. FCT2 and TCT2 were reduced in range of 1.5-2.7 msec and 0.5-2.3 msec due to load. DATA CONCLUSION: The proposed device is conditionally MR safe, low cost (material cost < INR 6000), portable, and effective in loading the knee joint with up to 50% of body weight. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation.

PURPOSE: Primary objective of this study was to retrospectively evaluate the potential of a range of qualitative and quantitative multiparametric features assessed on T2, post-contrast T1, DWI, DCE-MRI, and susceptibility-weighted-imaging (SWI) in differentiating evenly sampled cohort of primary-central-nervous-system-lymphoma (PCNSL) vs glioblastoma (GB) with pathological validation. METHODS: The study included MRI-data of histopathologically confirmed ninety-five GB and PCNSL patients scanned at 3.0 T MRI. A total of six qualitative features (three from T2 and post-contrast T1, three from SWI: thin-linear-uninterrupted-intra-tumoral-vasculature, broken-intra-tumoral-microvasculature, hemorrhage) were analyzed by three independent radiologists. Ten quantitative features from DWI and DCE-MRI were computed using in-house-developed algorithms. For qualitative features, Cohen's Kappa-interrater-variability-analysis was performed. Z-test and independent t-tests were performed to find significant qualitative and quantitative features respectively. Logistic-regression (LR) classifiers were implemented for evaluating performance of individual and various combinations of features in differentiating PCNSL vs GB. Performance evaluation was done via ROC-analysis. Pathological validation was performed to verify disintegration of vessel walls in GB and rim of viable neoplastic lymphoid cells with angiocentric-pattern in PCNSL. RESULTS: Three qualitative SWI features and four quantitative DCE-MRI features (rCBVcorr, Kep, Ve, and necrosis-volume-percentage) were significantly different (p < 0.05) between PCNSL and GB. Best diagnostic performance was observed with LR classifier using SWI features (AUC-0.99). The inclusion of quantitative features with SWI feature did not improve the differentiation accuracy. CONCLUSIONS: The combination of three qualitative SWI features using LR provided the highest accuracy in differentiating PCNSL and GB. Thin-linear-uninterrupted-intra-tumoral-vasculature in PCNSL and broken-intra-tumoral-microvasculature with hemorrhage in GB are the major contributors to the differentiation.

Poly(ADP-ribosyl)ation mediates early phase histone eviction at DNA lesions.

Nucleosomal histones are barriers to the DNA repair process particularly at DNA double-strand breaks (DSBs). However, the molecular mechanism by which these histone barriers are removed from the sites of DNA damage remains elusive. Here, we have generated a single specific inducible DSB in the cells and systematically examined the histone removal process at the DNA lesion. We found that histone removal occurred immediately following DNA damage and could extend up to a range of few kilobases from the lesion. To examine the molecular mechanism underlying DNA damage-induced histone removal, we screened histone modifications and found that histone ADP-ribosylation was associated with histone removal at DNA lesions. PARP inhibitor treatment suppressed the immediate histone eviction at DNA lesions. Moreover, we examined histone chaperones and found that the FACT complex recognized ADP-ribosylated histones and mediated the removal of histones in response to DNA damage. Taken together, our results reveal a pathway that regulates early histone barrier removal at DNA lesions. It may also explain the mechanism by which PARP inhibitor regulates early DNA damage repair.

Covid-19-associated rhino-orbito-cerebral mucormycosis (CAROM) in Delhi: Demographics and risk factors in a single-centre consecutive cohort of 200 inpatients.

Background There was a dramatic rise in the incidence of rhino-orbito-cerebral mucormycosis associated with the 2021 Covid-19 wave in India. We aim to document the demographic characteristics and risk factors of a consecutive cohort of inpatients with Covid-19-associated rhino-orbito-cerebral mucormycosis (CAROM) during the surge of April-June 2021. Methods We included all patients of CAROM treated at our tertiary referral facility from 1 April to 14 June 2021. We prospectively gathered details with regard to Covid-19 illness and treatment, CAROM presentation, comorbid conditions and risk factors. Results Our prospective cohort consisted of 200 consecutive patients, of which 146 (73%) patients tested positive on the Covid-19 RT-PCR test at presentation. CAROM occurred concurrent with the Covid-19 infection in 86%, and delayed CAROM after seeming recovery from Covid-19 was seen in 14%. Covid-19 was classified as mild, moderate and severe in 54%, 33% and 13%. The surge of CAROM followed the population peak of Covid-19 infections by about 3 weeks. Advanced disease at presentation was frequent with ocular involvement in 56.6% (111/196) and central nervous system involvement in 20% (40/199). One or more comorbid conditions were identified in 191/200 (95.5%) patients. The dominant associations were with diabetes (189/200; 94.5%) and uncontrolled hyper-glycaemia (122/133; 91.7%), recent steroid use (114/ 200; 57%), which was often considered as inappropriate in dosage or duration, lymphopenia (142/176; 80.7%), and increased ferritin levels (140/160; 87.5%). No evidence supported the role of previous oxygen therapy or previous nasal swab testing as risk factors for CAROM. Conclusion The inpatient volumes of CAROM were noted to parallel the Covid-19 incidence curve by about 3 weeks. Covid-19 infection may directly predispose to CAROM by way of lymphopenia and increased ferritin levels. Uncontrolled hyperglycaemia is identified as a near-invariable association. Recent steroid use is noted as very frequent and was often received in excess of treatment advisories.

Time versus tissue: Timely identification of Scedosporium Rhinosinusitis in a post-COVID-19 case by MALDI-TOF MS leading to successful management.

COVID-19 (Coronavirus Disease 2019), illness with associated comorbidities and corticosteroid therapy makes the host immunocompromised and prone to opportunistic microbial infections. As the world continues to struggle with the pandemic of COVID-19, an increase in cases of opportunistic fungal infections have been reported from all over the world during the second wave of COVID-19 like aspergillosis, mucormycosis, and candidiasis. Scedosporium apiospermum is an emerging pathogen that is usually associated with mycetoma, pulmonary infection, and central nervous infections. It has been rarely associated with fungal rhinosinusitis (FRS). In this study, a rare case of FRS caused by S.apiospermum in an immunocompromised post-Covid-19 diabetic woman is reported.

AI26 inhibits the ADP-ribosylhydrolase ARH3 and suppresses DNA damage repair.

The ADP-ribosylhydrolase ARH3 plays a key role in DNA damage repair, digesting poly(ADP-ribose) and removing ADP-ribose from serine residues of the substrates. Specific inhibitors that selectively target ARH3 would be a useful tool to examine DNA damage repair, as well as a possible strategy for tumor suppression. However, efforts to date have not identified any suitable compounds. Here, we used in silico and biochemistry screening to search for ARH3 inhibitors. We discovered a small molecule compound named ARH3 inhibitor 26 (AI26) as, to our knowledge, the first ARH3 inhibitor. AI26 binds to the catalytic pocket of ARH3 and inhibits the enzymatic activity of ARH3 with an estimated IC50 of ∼2.41 µm in vitro Moreover, hydrolysis of DNA damage-induced ADP-ribosylation was clearly inhibited when cells were pretreated with AI26, leading to defects in DNA damage repair. In addition, tumor cells with DNA damage repair defects were hypersensitive to AI26 treatment, as well as combinations of AI26 and other DNA-damaging agents such as camptothecin and doxorubicin. Collectively, these results reveal not only a chemical probe to study ARH3-mediated DNA damage repair but also a chemotherapeutic strategy for tumor suppression.

Model for in-vivo estimation of stiffness of tibiofemoral joint using MR imaging and FEM analysis.

BACKGROUND: Appropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject's tissue material properties are inaccessible. PURPOSE: The current study's purpose was to develop a methodology to estimate the subject-specific stiffness of the tibiofemoral joint using finite-element-analysis (FEA) and MRI data of knee joint with and without load. METHODS: In this study, six Magnetic Resonance Imaging (MRI) datasets were acquired from 3 healthy volunteers with axially loaded and unloaded knee joint. The strain was computed from the tibiofemoral bone gap difference (ΔmBGFT) using the knee MR images with and without load. The knee FEM study was conducted using a subject-specific knee joint 3D-model and various soft-tissue stiffness values (1 to 50 MPa) to develop subject-specific stiffness versus strain models. RESULTS: Less than 1.02% absolute convergence error was observed during the simulation. Subject-specific combined stiffness of weight-bearing tibiofemoral soft-tissue was estimated with mean values as 2.40 ± 0.17 MPa. Intra-subject variability has been observed during the repeat scan in 3 subjects as 0.27, 0.12, and 0.15 MPa, respectively. All subject-specific stiffness-strain relationship data was fitted well with power function (R2 = 0.997). CONCLUSION: The current study proposed a generalized mathematical model and a methodology to estimate subject-specific stiffness of the tibiofemoral joint for FEM analysis. Such a method might enhance the efficacy of FEM in implant design optimization and biomechanics for subject-specific studies. Trial registration The institutional ethics committee (IEC), Indian Institute of Technology, Delhi, India, approved the study on 20th September 2017, with reference number P-019; it was a pilot study, no clinical trail registration was recommended.

Effect of offset-frequency step size and interpolation methods on chemical exchange saturation transfer MRI computation in human brain.

Chemical exchange saturation transfer (CEST) MRI is a non-invasive molecular imaging technique with potential applications in pre-clinical and clinical studies. Applications of amide proton transfer-weighted (APT-w), glutamate-weighted (Glu-w) and creatine-weighted (Cr-w) CEST, among others, have been reported. In general, CEST data are acquired at multiple offset-frequencies. In reported studies, different offset-frequency step sizes and interpolation methods have been used during B0 inhomogeneity correction of data. The objective of the current study was to evaluate the effects of different step sizes and interpolation methods on CEST value computation. In the current study, simulation (Glu-w, Cr-w and APT-w) and experimental data from the brain were used. Experimental CEST data (Glu-w) were acquired from human volunteers at 7 T and brain tumor patients (APT-w) at 3 T. During B0 inhomogeneity correction, different interpolation methods (polynomial [degree-1, 2 and 3], cubic-Hermite, cubic-spline and smoothing-spline) were compared. CEST values were computed using asymmetry analysis. The effects of different step sizes and interpolation methods were evaluated using coefficient of variation (CV), normalized mean square error (nMSE) and coefficient of correlation parameters. Additionally, an optimum interpolation method for APT-w values was selected based upon fitting accuracy, T-test, receiver operating characteristic analysis, and its diagnostic performance in differentiating low-grade and high-grade tumors. CV and nMSE increase with an increase in step size irrespective of the interpolation method (except for cubic-Hermite and cubic-spline). The nMSE of Cr-w and Glu-w CEST values were least for polynomial (degree-2 and 3). The quality of Glu-w CEST maps became coarse with the increase in step size. There was a significant difference (P < .05) between low-grade and high-grade tumors using polynomial interpolation (degree-1, 2 and 3); however, linear interpolation outperforms other methods for APT-w data, providing the highest sensitivity and specificity. In conclusion, depending upon the saturation parameters and field strength, optimization of step size and interpolation should be carried out for different CEST metabolites/molecules. Glu-w, Cr-w and APT-w CEST data should be acquired with a step size of between 0.2 and 0.3 ppm. For B0 inhomogeneity correction, polynomial (degree-2) should be used for Glu-w and Cr-w CEST data at 7 T and linear interpolation should be used for APT-w data at 3 T for a limited frequency range.

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring.

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.