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Recurrent high-grade intracranial malignancies have a grim prognosis and uniform management guidelines are lacking. Re-irradiation is underused due to concerns about irreversible side effects. Pulsed-reduced dose rate radiotherapy (PRDR) aims to reduce toxicity while improving tumor control by exploiting dose-rate effects. We share our initial experience with temporally modulated pulsed proton re-irradiation (TMPPR), focusing on workflow, safety, feasibility, and outcomes for the first patient cohort. TMPPR was administered to patients with recurrent or progressive central nervous system malignancies using intensity modulated proton therapy with three fields. Patient and treatment data were collected, responses categorized using RANO assessment, and toxicities graded using CTCAE v5.0. Five patients received TMPPR between October 2022 and May 2023, with a median age of 54 years (Range: 32-72), and a median time from initial radiotherapy to re-RT of 23 months (Range 14-40). Treatment was completed without delay, with a median dose of 60 GyRBE in 30 fractions. Initial treatment response assessment showed complete (n = 1) or partial (n = 3) responses. Limited toxicity was observed, primarily grade 2 alopecia and one case of radiation necrosis graded at 2. This early experience demonstrates the feasibility of TMPPR delivery, highlighting the importance of prospective evaluations in the re-irradiation setting.

Cyanobacteria use large antenna complexes called phycobilisomes (PBSs) for light harvesting. However, intense light triggers non-photochemical quenching, where the orange carotenoid protein (OCP) binds to PBS, dissipating excess energy as heat. The mechanism of efficiently transferring energy from phycocyanobilins in PBS to canthaxanthin in OCP remains insufficiently understood. Using cryo-electron microscopy, we unveiled the OCP-PBS complex structure at 1.6- to 2.1-angstrom resolution, showcasing its inherent flexibility. Using multiscale quantum chemistry, we disclosed the quenching mechanism. Identifying key protein residues, we clarified how canthaxanthin's transition dipole moment in its lowest-energy dark state becomes large enough for efficient energy transfer from phycocyanobilins. Our energy transfer model offers a detailed understanding of the atomic determinants of light harvesting regulation and antenna architecture in cyanobacteria.

BACKGROUND: The choice of an appropriate strategy for intracanalicular vestibular schwannoma (ICVS) is still debated. We conducted a systematic review and meta-analysis with the aim to compare treatment outcomes amongst management strategies (conservative surveillance (CS), microsurgical resection (MR), or stereotactic radiosurgery (SRS)) aiming to inform guideline recommendations on behalf of the International Stereotactic Radiosurgery Society (ISRS). METHODS: Using PRISMA guidelines, we reviewed manuscripts published between January 1990 and October 2021 referenced in PubMed or Embase. Inclusion criteria were peer-reviewed clinical studies or case series reporting a cohort of ICVS managed with CS, MR, or SRS. Primary outcome measures included tumor control, the need for additional treatment, hearing outcomes, and posttreatment neurological deficits. These were pooled using meta-analytical techniques and compared using meta-regression with random effect. RESULTS: Forty studies were included (2371 patients). The weighted pooled estimates for tumor control were 96% and 65% in SRS and CS series, respectively (P

Abstract Prematurity-associated lung disease (PLD) is a long-term consequence of preterm-birth. Since the underlying mechanisms of PLD remain poorly characterised, we compared the urinary metabolome between recently described spirometry phenotypes of PLD. Preterm- and term-born children aged 7–12 years, from the Respiratory Health Outcomes in Neonates (RHiNO) cohort, underwent spirometry and urine collection. The urinary metabolome was analysed by gas chromatography time-of-flight mass spectrometry. Preterm-born children were classified into phenotypes of prematurity-associated obstructive lung disease (POLD, Forced expiratory volume in 1 s (FEV1)

BACKGROUND: Early palliative/pre-emptive intervention improves clinical outcomes and quality of life for patients with metastatic cancer. A previous signal-seeking randomized controlled trial (RCT) demonstrated that early upfront radiotherapy to asymptomatic or minimally symptomatic high-risk osseous metastases led to reduction in skeletal-related events (SREs), a benefit driven primarily by subgroup of high-risk spine metastasis. The current RCT aims to determine whether early palliative/pre-emptive radiotherapy in patients with high-risk, asymptomatic or minimally symptomatic spine metastases will lead to fewer SREs within 1 year. METHODS: This is a single-center, parallel-arm, in-progress RCT in adults (≥ 18 years) with ECOG performance status 0-2 and asymptomatic or minimally symptomatic (not requiring opioids) high-risk spine metastases from histologically confirmed solid tumor malignancies with > 5 sites of metastatic disease on cross-sectional imaging. High-risk spine metastases are defined by the following: (a) bulkiest disease sites ≥ 2 cm; (b) junctional disease (occiput to C2, C7-T1, T12-L2, L5-S1); (c) posterior element involvement; or (d) vertebral body compression deformity > 50%. Patients are randomized 1:1 to receive either standard-of-care systemic therapy (arm 1) or upfront, early radiotherapy to ≤ 5 high-risk spine lesions plus standard-of-care systemic therapy (arm 2), in the form of 20-30 Gy of radiation in 2-10 fractions. The primary endpoint is SRE, a composite outcome including spinal fracture, spinal cord compression, need for palliative radiotherapy, interventional procedures, or spinal surgery. Secondary endpoints include (1) surrogates of health care cost, including the number and duration of SRE-related hospitalizations; (2) overall survival; (3) pain-free survival; and (4) quality of life. Study instruments will be captured pre-treatment, at baseline, during treatment, and at 1, 3, 6, 12, and 24 months post-treatment. The trial aims to accrue 74 patients over 2 years to achieve > 80% power in detecting difference using two-sample proportion test with alpha

Abstract Trastuzumab-deruxtecan (T-DXd) has demonstrated intracranial efficacy; however, safety and efficacy data remains limited with stereotactic radiosurgery (SRS). A multi-institutional review was performed with HER2+ or HER2-low metastatic breast cancer treated with T-DXd and SRS for active brain metastases. We identified 215 lesions treated over 48 SRS courses in 34 patients. Median follow up from T-DXd initiation was 13.9 months. The cumulative incidence of symptomatic radiation necrosis at 24 months per lesion was 2.1% and per patient 11%. The 12-month LC was 97%. HER2-low was associated with worse distant intracranial control (DIC) (adjusted HR 2.5, 95% CI 1.1–5.6, p = 0.03) and worse systemic progression free survival (PFS) (HR 4.1, 95% CI 1.6–10.7, p = 0.004). Concurrent SRS and T-DXd has excellent local control, without an increased risk of radiation necrosis. HER2-low disease is associated with worse systemic PFS and DIC with T-DXd compared to HER2+.

Abstract Recurrent high-grade intracranial malignancies have a grim prognosis and uniform management guidelines are lacking. Re-irradiation is underused due to concerns about irreversible side effects. Pulsed-reduced dose rate radiotherapy (PRDR) aims to reduce toxicity while improving tumor control by exploiting dose-rate effects. We share our initial experience with temporally modulated pulsed proton re-irradiation (TMPPR), focusing on workflow, safety, feasibility, and outcomes for the first patient cohort. TMPPR was administered to patients with recurrent or progressive central nervous system malignancies using intensity modulated proton therapy with three fields. Patient and treatment data were collected, responses categorized using RANO assessment, and toxicities graded using CTCAE v5.0. Five patients received TMPPR between October 2022 and May 2023, with a median age of 54 years (Range: 32–72), and a median time from initial radiotherapy to re-RT of 23 months (Range 14–40). Treatment was completed without delay, with a median dose of 60 GyRBE in 30 fractions. Initial treatment response assessment showed complete (n = 1) or partial (n = 3) responses. Limited toxicity was observed, primarily grade 2 alopecia and one case of radiation necrosis graded at 2. This early experience demonstrates the feasibility of TMPPR delivery, highlighting the importance of prospective evaluations in the re-irradiation setting.

Abstract Metformin is an antihyperglycemic used to treat type 2 diabetes mellitus (T2DM). Patients with T2DM are at increased risk of cardiovascular disease. We explored the association between metformin use and cardiovascular magnetic resonance (CMR) derived stress myocardial blood flow (MBF), myocardial perfusion reserve (MPR) and major adverse cardiovascular events (MACE; all cause death, MI, stroke, heart failure hospitalisation and coronary revascularisation) in patients with T2DM. Multi-centre study of patients with T2DM, and healthy controls, underwent quantitative myocardial perfusion CMR using an artificial intelligence supported process. Multivariable regression analysis, and cox proportional hazard models of propensity score weighted patients quantified associations between metformin use, MBF, MPR, all cause death and MACE. Analysis included 572 patients with T2DM (68% prescribed metformin) with median follow-up 851 days (IQR 935 − 765). Metformin use was associated with an increase of MPR of 0.12 [0.08–0.40], p = 0.004. There were 82 MACE events (14.3%) including 25 (4.4%) deaths of which 16 were in those not prescribed metformin (8.7%), compared to 9 in patients prescribed metformin (2.3%): adjusted hazard ratio 0.24 (95% CI 0.08–0.70, p = 0.009). MACE events were similar between groups. This multicentre, inverse probability weighting propensity score analysis study showed that in patients with T2DM, metformin use is associated with higher MPR and improved all cause survival.

Parkinson's disease (PD) is the second most prevalent neurological disorder, predominantly affecting older people. With no existing cure, the early detection of PD, where symptoms are not entirely evident but indicative of the disease's onset, is critical. This study aims to design and develop AI-based diagnostic methods that can detect these early signs of PD with high accuracy, thereby facilitating more effective disease management. This study focuses on developing a method that not only identifies PD at an early stage but also provides clinicians with a tool to interpret the decisions taken by the AI models to avoid misdiagnosis. In this study, a T2-weighted 3D Magnetic resonance imaging (MRI) dataset is used to analyze detailed morphological, textural, and structural changes. The MRI scans are pre-processed using brain extraction, image registration, bias correction, normalization, and segmentation processes. Upon segmentation, feature extraction was applied to the segmented subcortical regions using radiomics tools, resulting in the extraction of 107 features. The top 20 features were selected through Pearson’s correlation, recursive feature elimination, and a ranking model, which are responsible for the ML model’s class prediction. Statistical validation of these features was also performed using Analysis of Variance (ANOVA), pairwise t-tests, and Kruskal-Wallis H-tests to ensure that the identified 20 features were dominant for the prediction. Based on the identified features, several Machine Learning (ML) models were used to identify the best classifier for the provided real-world MRI scans. The Gradient Boosting (GB) algorithm achieved better prediction accuracy among the compared models. Incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalances significantly improved the model's performance, boosting accuracy to 96.8 % from 87 %. Further, multiple Explainable Artificial Intelligence (XAI) techniques were deployed to enhance the transparency and interpretability of the models. These techniques provide insights into how each identified feature influences predictions by the classifier, assisting clinicians in making trustworthy decisions when planning diagnosis and treatment measures.

Abstract Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market. GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.git

Abstract In this study, the research presents the Federated Learning Enhanced Multi-Layer Perceptron (Fed-MLP) Long Short-Term Memory that is suggested by the research. The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learning in an endeavor to augment the predictive competency. In the first step, we gather stock market indices from various financial organizations, using CAC40 stocks as the index for the French stock market. To guarantee data consistency and quality, pre-processing methods including linear interpolation and Z-score normalization are used. There are two types of models for each of the three basic elements within the Fed-MLP–LSTM, namely, MLP for feature extraction and LSTM for sequence modeling. Institutionally, each refining institution trains a local MLP–LSTM on the corpus specific to their institution, with only the model parameters being transferred to a central server through Federated Learning. A global model is created and updated through repeated training and totaling of parameters while preserving privacy of the data going to each node. In the performance evaluation, quantitative measures like Root-Mean-Square Error (RMSE), and accuracy are seven used. Hypothesis testing shows that we have good evidence to support that the proposed Fed-MLP–LSTM outperforms the other methods with the lowest RMSE of 0. 0108 and 98.3% of accuracy with reference to their respective cocaine molecule target. The proposed method is implemented in python. This suggests that using Federated Learning along with MLP and LSTM as the components of this vector enhanced the function increasing its capacity and reliability in predicting the trends of stocks. In conclusion, the present study suggests a sound solution for effective and secure stock market forecasting in collaboration environments that can find its use in the financial domains and securities businesses.

Abstract Parkinson’s disease (PD) is one of the most common neurodegenerative disorders that affect the quality of human life of millions of people throughout the world. The probability of getting affected by this disease increases with age, and it is common among the elderly population. Early detection can help in initiating medications at an earlier stage. It can significantly slow down the progression of this disease, assisting the patient to maintain a good quality of life for a more extended period. Magnetic resonance imaging (MRI)-based brain imaging is an area of active research that is used to diagnose PD disease early and to understand the key biomarkers. The prior research investigations using MRI data mainly focus on volume, structural, and morphological changes in the basal ganglia (BG) region for diagnosing PD. Recently, researchers have emphasized the significance of studying other areas of the human brain for a more comprehensive understanding of PD and also to analyze changes happening in brain tissue. Thus, to perform accurate diagnosis and treatment planning for early identification of PD, this work focuses on learning the onset of PD from images taken from whole-brain MRI using a novel 3D-convolutional neural network (3D-CNN) deep learning architecture. The conventional 3D-Resent deep learning model, after various hyper-parameter tuning and architectural changes, has achieved an accuracy of 90%. In this work, a novel 3D-CNN architecture was developed, and after several ablation studies, the model yielded results with an improved accuracy of 93.4%. Combining features from the 3D-CNN and 3D ResNet models using Canonical Correlation Analysis (CCA) resulted in 95% accuracy. For further enhancements of the model performance, feature fusion with optimization was employed, utilizing various optimization techniques. Whale optimization based on a biologically inspired approach was selected on the basis of a convergence diagram. The performance of this approach is compared to other methods and has given an accuracy of 97%. This work represents a critical advancement in improving PD diagnosis techniques and emphasizing the importance of deep nested 3D learning and bio-inspired feature selection.

Abstract Household electricity consumption (HEC) is changing over time, depends on multiple factors, and leads to effects on the prediction accuracy of the model. The objective of this work is to propose a novel methodology for improving HEC prediction accuracy. This study uses two original datasets, namely questionnaire survey (QS) and monthly consumption (MC), which contain data from 225 consumers from Maharashtra, India. The original datasets are combined to create three additional datasets, namely QS + MC, QS equation (QsEq) + next month’s consumptions, and QsEq + MC. Furthermore, the HEC prediction accuracy is boosted by applying different approaches, like correlation methods, feature engineering techniques, data quality assessment, heterogeneous ensemble prediction (HEP), and the hybrid model. Five HEP models are created using dataset combinations and machine learning algorithms. Based on the MC dataset, the random forest provides the best prediction of RMSE (36.18 kWh), MAE (25.73 kWh), and R2 (0.76). Similarly, QsEq + MC dataset adaptive boosting provides a better prediction of RMSE (36.77 kWh), MAE (26.18 kWh), and R2 (0.76). This prediction accuracy is further increased using the proposed hybrid model to RMSE (22.02 kWh), MAE (13.04 kWh), and R2 (0.92). This research work benefits researchers, policymakers, and utility companies in obtaining accurate prediction models and understanding HEC.

Background: The aim of the study was to describe the epidemiology and study the risk factors, clinical presentation, management, and outcome of rhino-orbital-cerebral mucormycosis (ROCM) in terms of mortality, exenteration, eye salvage, and vision salvage. Methods: This retrospective, observational study was carried out over a period of two months. A detailed history was noted, and an ophthalmological examination was done. The diagnosis was done by Potassium hydroxide (KOH) mount and fungal culture. Magnetic resonance imaging (MRI) of the orbit, brain, and paranasal sinuses were performed. Medical (intravenous amphotericin B, posaconazole), surgical (retrobulbar amphotericin B injection, exenteration, Functional Endoscopic Sinus Surgery (FESS)), or combined management was evaluated, and clinical outcomes was noted. Results: The mean age of patients was 54.2 years and the male-to-female ratio was 1.77/1. The most common underlying risk factor for ROCM was uncontrolled diabetes mellitus (70%), followed by the use of corticosteroids for the management of coronavirus disease 2019 (COVID-19) infection in 68% of patients. The most common clinical presentation was diminution of vision followed by eschar, ptosis, and proptosis. Medical and FESS were done in all patients; exenteration was done in 12% of patients. Sixty-six percent of patients were alive with regression of ROCM, 20% of patients were alive with residual, 8% of patients were alive with the progression of ROCM, and 6% of patients had expired. Among the ones who are alive, the ocular outcome was orbital exenteration in 12.76%, the eye was salvaged in 25.53 and vision salvage was achieved in 61.70%. Conclusion: ROCM affects older males. Immunosuppression due to COVID-19 infection, diabetes mellitus, and corticosteroid use in the management of COVID-19 are the main risk factors for the development of ROCM. Antifungal therapy along with surgical debridement decreases mortality.

Abstract Background Obstetric Bleeding Study UK (OBS UK) (award ID: 152057) is a National Institute for Health and Care Research (NIHR)-funded stepped wedge cluster randomised controlled trial of a complex intervention for postpartum haemorrhage. This was developed in Wales and evaluated in a feasibility study, with improvements in maternal outcomes observed. Generalisability of the findings is limited by lack of control data and limited ethnic diversity in the Welsh obstetric patient population compared to the United Kingdom (UK): 94% of the Welsh population identifies as White, versus 82% in the UK. Non-White ethnicity and socioeconomic deprivation are linked to increased risk of adverse maternal outcomes. Traditionally, decisions regarding site selection are based on desire to complete trials on target in ‘tried and tested’ research active institutions. To ensure widespread applicability of the results and investigate the impact of ethnicity and social deprivation on trial outcomes, maternity units were recruited that represent the ethnic diversity and social deprivation profiles of the UK. Method Using routinely collected, publicly available data, an interactive dashboard was developed that demonstrates the demographics of the population served by each maternity unit in the UK, to inform site recruitment. Data on births per year, ethnic and socioeconomic group of the population for each maternity unit, across the UK, were integrated into the dashboard. Results The dashboard demonstrates that OBS UK trial sites reflect the ethnic and socioeconomic diversity of the UK (study vs UK population ethnicity: White 79.2% vs 81.7%, Asian 10.5% vs 9.3%, Black 4.7% vs 4.0%, Mixed 2.5% vs 2.9%, Other 3.0% vs 2.1%) with variation in site demography, size and location. Missing data varied across sites and nations and is presented. Conclusion The NIHR equality, diversity and inclusion strategy states studies must widen participation, facilitating individuals from all backgrounds to engage. The development of this novel interactive dashboard demonstrates an innovative way of achieving this. National Health Service (NHS) maternity researchers should consider using this tool to enhance diversity in research, address health disparities and improve generalisability of findings. This approach could be applied to healthcare settings beyond maternity care and across different global populations. Trial registration ISRCTN 17679951. Registered on August 30, 2023.

Surgical resection represents the standard of care for people with newly diagnosed diffuse gliomas, and the neuropathological and molecular profile of the resected tissue guides clinical management and forms the basis for research. The Response Assessment in Neuro-Oncology (RANO) consortium is an international, multidisciplinary effort that aims to standardise research practice in neuro-oncology. These recommendations represent a multidisciplinary consensus from the four RANO groups: RANO resect, RANO recurrent glioblastoma, RANO radiotherapy, and RANO/PET for a standardised workflow to achieve a representative tumour evaluation in a disease characterised by intratumoural heterogeneity, including recommendations on which tumour regions should be surgically sampled, how to define those regions on the basis of preoperative imaging, and the optimal sample volume. Practical recommendations for tissue sampling are given for people with low-grade and high-grade gliomas, as well as for people with newly diagnosed and recurrent disease. Sampling of liquid biopsies is also addressed. A standardised workflow for subsequent handling of the resected tissue is proposed to avoid information loss due to decreasing tissue quality or insufficient clinical information. The recommendations offer a framework for prospective biobanking studies.

PURPOSE: The purpose of this study is to investigate inter-planner plan quality variability using a manual forward planning (MFP)- or fast inverse planning (FIP, Lightning)-approach for single brain lesions treated with the Gamma Knife® (GK) Icon™. METHODS: Thirty patients who were previously treated with GK stereotactic radiosurgery or radiotherapy were selected and divided into three groups (post-operative resection cavity, intact brain metastasis, and vestibular schwannoma [10 patients per group]). Clinical plans for the 30 patients were generated by multiple planners using FIP only (1), a combination of FIP and MFP (12), and MFP only (17). Three planners (Senior, Junior, and Novice) with varying experience levels re-planned the 30 patients using MFP and FIP (two plans per patient) with planning time limit of 60 min. Statistical analysis was performed to compare plan quality metrics (Paddick conformity index, gradient index, number of shots, prescription isodose line, target coverage, beam-on-time (BOT), and organs-at-risk doses) of MFP or FIP plans among three planners and to compare plan quality metrics between each planners MFP/FIP plans and clinical plans. Variability in FIP parameter settings (BOT, low dose, and target max dose) and in planning time among the planners was also evaluated. RESULTS: Variations in plan quality metrics of FIP plans among three planners were smaller than those of MFP plans for all three groups. Juniors MFP plans were the most comparable to the clinical plans, whereas Seniors and Novices MFP plans were superior and inferior, respectively. All three planners FIP plans were comparable or superior to the clinical plans. Differences in FIP parameter settings among the planners were observed. Planning time was shorter and variations in planning time among the planners were smaller for FIP plans in all three groups. CONCLUSIONS: The FIP approach is less planner dependent and more time-honored than the MFP approach.