Pharmaceutical company funding of cancer patient advocacy organizations in the Netherlands.

BACKGROUND: Financial conflicts of interest (FCOI) of medical professionals and associated organizations with pharmaceutical companies (pharma) might contribute to the use of low value oncological treatments. Value criteria for oncological drug approvals in the Netherlands have recently become more stringent leading to objections by cancer patient advocacy organizations (cPAOs). Considering the importance of cPAOs input in cancer patient care we analyzed whether pharma funding of cPAOs occurs in the Netherlands. METHODS: The cPAO websites and available annual reports were evaluated for disclosure of pharma funding for the years 2021 and 2022. Also, data from the Dutch Healthcare Transparency Registry (DHTR) were extracted. RESULTS: Twenty-one of 34 (61.8 %) cPAOs received pharma funding (with 20 registered in the DHTR), and for 13 (29.4 %) cPAOs no reporting of pharma funding could be found. Three of the cPAOs disclosed pharma funding directly on their main website. Online educational material was available from 22 cPAOs on their websites with pharma funding disclosed on the educational material in 5. The total registered amount of pharmaceutical funding was €667,232.00 in 2021 and €536,098.00 in 2022. The median (and interquartile ranges) DHTR registered amount of support per cPAO that received funding in the studied period was €23,799.50 (14,823.75-84,663.30). The most common funding category as defined in the DHTR was project sponsorship. CONCLUSIONS: Financial support by the pharmaceutical industry is common for Dutch cPAOs. Given the importance of cPAOs and their objective input in the societal debate on the availability of cancer drugs, the potential influence of pharma sponsoring should be critically evaluated.

Nanoironing van der Waals Heterostructures toward Electrically Controlled Quantum Dots.

Assembling two-dimensional van der Waals (vdW)-layered materials into heterostructures is an exciting development that sparked the discovery of rich correlated electronic phenomena. vdW heterostructures also offer possibilities for designer device applications in areas such as optoelectronics, valley- and spintronics, and quantum technology. However, realizing the full potential of these heterostructures requires interfaces with exceptionally low disorder which is challenging to engineer. Here, we show that thermal scanning probes can be used to create pristine interfaces in vdW heterostructures. Our approach is compatible at both the material- and device levels, and monolayer WS2 transistors show up to an order of magnitude improvement in electrical performance from this technique. We also demonstrate vdW heterostructures with low interface disorder enabling the electrical formation and control of quantum dots that can be tuned from macroscopic current flow to the single-electron tunneling regime.

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry.

Introduction: Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. Methods: Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. Results: In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. Discussion: We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease.

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting 'brain age' - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

Practice and proficiency of Isha Yoga for better mental health outcomes: insights from a COVID-19 survey.

Introduction: The COVID-19 pandemic has brought about unparalleled suffering on a global scale, affecting both physical and mental well-being. In such challenging times, it becomes crucial to identify interventions that can alleviate negative mental health outcomes, such as stress, while promoting positive mental health outcomes, like well-being. We report the effectiveness of a mind-body practise, Isha Yoga, in promoting well-being. Methods: We conducted an online survey, during the COVID-19 pandemic, with Yoga practitioners (n = 1,352) from the Isha Yoga tradition in Karnataka, India. We evaluated stress and well-being attributes using conventional psychometric questionnaires. Subsequently, we requested the Isha Yoga practitioners to share another survey with their friends and family members, assessing similar outcomes. From the respondents of this shared survey (n = 221), we identified individuals who currently did not engage in any form of Yoga or meditation, constituting the non-Yoga control group (n = 110). To enhance the reliability and validity of our study and minimize the limitations commonly associated with online surveys, we adhered to the CHERRIES guidelines for reporting survey studies. Results: Isha Yoga practitioners had significantly lower levels of stress (p < 0.001, gHedges = 0.94) and mental distress (p < 0.001, gHedges = 0.75) while reporting significantly higher levels of well-being (p < 0.001, gHedges = 0.78) and affective balance (p < 0.001, gHedges = 0.80) compared to the control group. Furthermore, expertise-related improvements were observed in these outcomes, and a dose-response relationship was found between regularity of Isha Yoga practice and outcome changes. A minimum 3-4 days of weekly practice showed significant differences with the control group. In addition, we investigated the effect of Isha Yoga on stress and well-being among the healthcare workers (HCWs) in our sample and observed better mental health outcomes. Discussion: These findings collectively underscore the benefits of Mind and Body practices like Isha Yoga on various aspects of mental health and well-being, emphasizing its potential as an effective and holistic approach for promoting a healthy lifestyle among diverse populations, including healthcare workers, even in difficult circumstances such as the COVID-19 pandemic.

Photocatalytic degradation of organic dyes using reduced graphene oxide (rGO).

Photocatalysts have developed into a successful strategy for degrading synthetic and organic toxins, such as chemicals and dyes, in wastewater. In this study, graphene oxide was reduced at different temperatures and used for degrading indigo carmine and neutral red dyes. The wide surface areas, strong adsorption sites, and oxygen functionalities of reduced graphene oxide (rGO) at 250 °C (rGO-250) produced more photocatalytic degradation efficiency and adsorption percentage. The catalyst dosage, initial dye concentration, solution pH and recyclability were all used to optimize the photocatalytic activity of rGO-250. This research presents a capable nano-adsorbent photocatalyst for the efficient degradation of organic dyes. GO and rGOs were also investigated for carbon dioxide (CO2) absorption properties. Results showed that rGO-250 has better CO2 adsorption properties than other rGOs. Overall, it was observed that rGO-250 has better photocatalytic and CO2 adsorption capabilities compared to graphene oxide reduced at different temperatures.

Dose response of transcranial near infrared light stimulation on brain functional connectivity and cognition in older adults-A randomized comparison.

Photobiomodulation, also called low-level light therapy, has been reported in animal studies to have an effect on brain activity and cognition. However, studies in humans regarding its effect on cognition and brain functional connectivity, and the required dose threshold for achieving the same have been very limited. We compared the effects of different doses of photobiomodulation (PBM) on cognition and resting state brain functional connectivity in 25 cognitively normal adults aged 55-70 years. They were randomized to a single session of the sham group, "low-dose" and "high-dose" groups receiving NIR light with transcranial fluence of 26 and 52 J/cm2 respectively, and intranasal fluence of 9 and 18 J/cm2 respectively. There was a significant increase in resting state functional connectivity of the left superior frontal gyrus (SFG) with the left planum temporale (PT), p = 0.0016, and with the left inferior frontal gyrus, pars triangularis, p = 0.0235 in the "high-dose" group only compared to the "sham" group. There was also a significant improvement in visual search and processing speed (p = 0.012) in the "high-dose" group. Replication of these findings in an adequately powered randomized sham-controlled study in healthy older adults can pave the way for clinical application of NIRL as a therapeutic modality in patients with Alzheimer's disease.

Probing charge traps at the 2D semiconductor/dielectric interface.

The family of 2-dimensional (2D) semiconductors is a subject of intensive scientific research due to their potential in next-generation electronics. While offering many unique properties like atomic thickness and chemically inert surfaces, the integration of 2D semiconductors with conventional dielectric materials is challenging. The charge traps at the semiconductor/dielectric interface are among many issues to be addressed before these materials can be of industrial relevance. Conventional electrical characterization methods remain inadequate to quantify the traps at the 2D semiconductor/dielectric interface since the estimations of the density of interface traps, Dit, by different techniques may yield more than an order-of-magnitude discrepancy, even when extracted from the same device. Therefore, the challenge to quantify Dit at the 2D semiconductor/dielectric interface is about finding an accurate and reliable measurement method. In this review, we discuss characterization techniques which have been used to study the 2D semiconductor/dielectric interface. Specifically, we discuss the methods based on small-signal AC measurements, subthreshold slope measurements and low-frequency noise measurements. While these approaches were developed for silicon-based technology, 2D semiconductor devices possess a set of unique challenges requiring a careful re-evaluation when using these characterization techniques. We examine the conventional methods based on their efficacy and accuracy in differentiating various types of trap states and provide guidance to find an appropriate method for charge trap analysis and estimation of Dit at 2D semiconductor/dielectric interfaces.

Untoward global effects of current guideline formulation of stereotactic radiotherapy for symptomatic brain metastases by international medical societies.

The quality of evidence leading to new oncological treatments suffers shortcomings, as has recently been addressed for drug approvals. In this 'Personal view', we evaluate the unintended effects of adopting stereotactic radiosurgery as the standard of care for patients with limited number of symptomatic brain metastases and favourable prognostic factors in international guidelines in view of the limitations in the evidence of efficacy and effectiveness, with special focus on countries with relatively limited resources.

BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data.

We present BundleCleaner, an unsupervised multi-step framework that can filter, denoise and subsample bundles derived from diffusion MRI-based whole-brain tractography. Our approach considers both the global bundle structure and local streamline-wise features. We apply BundleCleaner to bundles generated from single-shell diffusion MRI data in an independent clinical sample of older adults from India using probabilistic tractography and the resulting 'cleaned' bundles can better align with the atlas bundles with reduced overreach. In a downstream tractometry analysis, we show that the cleaned bundles, represented with less than 20% of the original set of points, can robustly localize along-tract microstructural differences between 32 healthy controls and 34 participants with Alzheimer's disease ranging in age from 55 to 84 years old. Our approach can help reduce memory burden and improving computational efficiency when working with tractography data, and shows promise for large-scale multi-site tractometry.

Impact of COVID-19 on heart failure hospitalization and outcome in India - A cardiological society of India study (CSI-HF in COVID 19 times study - "The COVID C-HF study").

OBJECTIVES: The presentation and outcomes of acute decompensated heart failure (ADHF) during COVID times (June 2020 to Dec 2020) were compared with the historical control during the same period in 2019. METHODS: Data of 4806 consecutive patients of acute HF admitted in 22 centres in the country were collected during this period. The admission patterns, aetiology, outcomes, prescription of guideline-directed medical therapy (GDMT) and interventions were analysed in this retrospective study. RESULTS: Admissions for acute heart failure during the pandemic period in 2020 decreased by 20% compared to the corresponding six-month period in 2019, with numbers dropping from 2675 to 2131. However, no difference in the epidemiology was seen. The mean age of presentation in 2019 was 61.75 (±13.7) years, and 59.97 (±14.6) years in 2020. There was a significant decrease in the mean age of presentation (p = 0.001). Also. the proportion of male patients decreased significantly from 68.67% to 65.84% (p = 0.037). The in-hospital mortality for acute heart failure did not differ significantly between 2019 and 2020 (4.19% and 4.,97%) respectively (p = 0.19). The proportion of patients with HFrEF did not change in 2020 compared to 2019 (76.82% vs 75.74%, respectively). The average duration of hospital stay was 6.5 days. CONCLUSION: The outcomes of ADHF patients admitted during the Covid pandemic did not differ significantly. The length of hospital stay remained the same. The study highlighted the sub-optimal use of GDMT, though slightly improving over the last few years.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Cardiovascular autonomic neuropathy is associated with increased glycemic variability driven by hyperglycemia rather than hypoglycemia in patients with diabetes.

AIM: Cardiac autonomic neuropathy (CAN) has been suggested to be associated with hypoglycemia and impaired hypoglycemia unawareness. We have assessed the relationship between CAN and extensive measures of glucose variability (GV) in patients with type 1 and type 2 diabetes. METHODS: Participants with diabetes underwent continuous glucose monitoring (CGM) to obtain measures of GV and the extent of hyperglycemia and hypoglycemia and cardiovascular autonomic reflex testing. RESULTS: Of the 40 participants (20 T1DM and 20 T2DM) (aged 40.70 ± 13.73 years, diabetes duration 14.43 ± 7.35 years, HbA1c 8.85 ± 1.70%), 23 (57.5%) had CAN. Despite a lower coefficient of variation (CV) (31.26 ± 11.87 vs. 40.33 ± 11.03, P = 0.018), they had a higher CONGA (8.42 ± 2.58 vs. 6.68 ± 1.88, P = 0.024) with a lower median LBGI (1.60 (range: 0.20-3.50) vs. 4.90 (range: 3.20-7.40), P = 0.010) and percentage median time spent in hypoglycemia (4 (range:4-13) vs. 1 (range:0-5), P = 0.008), compared to those without CAN. The percentage GRADEEuglycemia (3.30 ± 2.78 vs. 5.69 ± 3.09, P = 0.017) and GRADEHypoglycemia (0.3 (range: 0 - 3.80) vs. 1.8 (range: 0.9-6.5), P = 0.036) were significantly lower, while the percentage median GRADEHyperglycemia (95.45 (range:93-98) vs. 91.6 (82.8-95.1), P = 0.013) was significantly higher in participants with CAN compared to those without CAN. CONCLUSION: CAN was associated with increased glycemic variability with less time in euglycemia attributed to a greater time in hyperglycemia but not hypoglycemia.

Patterns of Impaired Neurocognitive Performance on the Global Neuropsychological Assessment, and Their Brain Structural Correlates in Recent-onset and Chronic Schizophrenia.

Objective: Schizophrenia is associated with impairment in multiple cognitive domains. There is a paucity of research on the effect of prolonged illness duration (≥ 15 years) on cognitive performance along multiple domains. In this pilot study, we used the Global Neuropsychological Assessment (GNA), a brief cognitive battery, to explore the patterns of cognitive impairment in recent-onset (≤ 2 years) compared to chronic schizophrenia (≥ 15 years), and correlate cognitive performance with brain morphometry in patients and healthy adults. Methods: We assessed cognitive performance in patients with recent-onset (n = 17, illness duration ≤ 2 years) and chronic schizophrenia (n = 14, duration ≥ 15 years), and healthy adults (n = 16) using the GNA and examined correlations between cognitive scores and gray matter volumes computed from T1-weighted magnetic resonance imaging images. Results: We observed cognitive deficits affecting multiple domains in the schizophrenia samples. Selectively greater impairment of perceptual comparison speed was found in adults with chronic schizophrenia (p = 0.009, η2partial = 0.25). In the full sample (n = 47), perceptual comparison speed correlated significantly with gray matter volumes in the anterior and medial temporal lobes (TFCE, FWE p < 0.01). Conclusion: Along with generalized deficit across multiple cognitive domains, selectively greater impairment of perceptual comparison speed appears to characterize chronic schizophrenia. This pattern might indicate an accelerated or premature cognitive aging. Anterior-medial temporal gray matter volumes especially of the left hemisphere might underlie the impairment noted in this domain in schizophrenia.

Revealing the improved sensitivity of PEDOT:PSS/PVA thin films through secondary doping and their strain sensors application.

The field of strain sensing involves the ability to measure an electrical response that corresponds to a strain. The integration of synthetic and conducting polymers can create a flexible strain sensor with a wide range of applications, including soft robotics, sport performance monitoring, gaming and virtual reality, and healthcare and biomedical engineering. However, the use of insulating synthetic polymers can impede the semiconducting properties of sensors, which may reduce sensor sensitivity. Previous research has shown that the doping process can significantly enhance the electrical performance and ionic conduction of conducting polymers, thereby strengthening their potential for use in electronic devices. However the full effects of secondary doping on the crystallinity, stretchability, conductivity, and sensitivity of conducting polymer blends have not been studied. In this study, we investigated the effects of secondary doping on the properties of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)/poly(vinyl alcohol) (PEDOT:PSS/PVA) polymer blend thin films and their potential use as strain sensors. The thin films were prepared using a facile drop-casting method. Morphology analysis using profilometry and atomic force microscopy confirmed the occurrence of phase segregation and revealed surface roughness values. This evidence provided a comprehensive understanding of the chemical interactions and physical properties of the thin films, and the effects of doping on these properties. The best films were selected and applied as sensitive strain sensors. EG-PEDOT:PSS/PVA thin films showing a significant increase of conductivity values from the addition of 1 vol% to 12 vol% addition, with conductivity values of 8.51 × 10-5 to 9.42 × 10-3 S cm-1. Our 12% EG-PEDOT:PSS/PVA sensors had the highest GF value of 2000 too. We compared our results with previous studies on polymeric sensors, and it was found that our sensors quantitatively had better GF values. Illustration that demonstrates the DMSO and EG dopant effects on PEDOT:PSS structure through bonding interaction, crystallinity, thermal stability, surface roughness, conductivity and stretchability was also provided. This study suggests a new aspect of doping interaction that can enhance the conductivity and sensitivity of PEDOT:PSS for device applications.