Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

Rogue AI: Cautionary Cases in Neuroradiology and What We Can Learn From Them.

Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow. This paper explores AI's impact on neuroradiology, an area accounting for a substantial portion of recent radiology studies. We present a case series evaluating an AI software's performance in detecting neurovascular findings, highlighting five cases where AI interpretations differed from radiologists' assessments. Our study underscores common pitfalls of AI in the context of CT head angiograms, aiming to guide future AI algorithms.  Methods We conducted a retrospective case series study at Stony Brook University Hospital, a large medical center in Stony Brook, New York, spanning from October 1, 2021 to December 31, 2021, analyzing 140 randomly sampled CT angiograms using AI software. This software assessed various neurovascular parameters, and AI findings were compared with neuroradiologists' interpretations. Five cases with divergent interpretations were selected for detailed analysis. Results Five representative cases in which AI findings were discordant with radiologists' interpretations are presented with diagnoses including diffuse anoxic ischemic injury, cortical laminar necrosis, colloid cyst, right superficial temporal artery-to-middle cerebral artery (STA-MCA) bypass, and subacute bilateral subdural hematomas. Discussion The errors identified in our case series expose AI's limitations in radiology. Our case series reveals that AI's incorrect interpretations can stem from complexities in pathology, challenges in distinguishing densities, inability to identify artifacts, identifying post-surgical changes in normal anatomy, sensitivity limitations, and insufficient pattern recognition. AI's potential for improvement lies in refining its algorithms to effectively recognize and differentiate pathologies. Incorporating more diverse training datasets, multimodal data, deep-reinforcement learning, clinical context, and real-time learning capabilities are some ways to improve AI's performance in the field of radiology. Conclusion Overall, it is apparent that AI applications in radiology have much room for improvement before becoming more widely integrated into clinical workflows. While AI demonstrates remarkable potential to aid in diagnosis and streamline workflows, our case series highlights common pitfalls that underscore the need for continuous improvement. By refining algorithms, incorporating diverse datasets, embracing multimodal information, and leveraging innovative machine learning strategies, AI's diagnostic accuracy can be significantly improved.

The Acute Effect of Dynamic vs. Proprioceptive Neuromuscular Facilitation Stretching on Sprint and Jump Performance.

Participating in sports has been shown to promote overall wellness and, at the same time, reduce health risks. As more people are participating in sports, competitions have increased, and every aspect of the game has been focused by coaches and athletes in order to improve performance. One of these aspects is the warm-up session. The purpose of this study was to investigate the acute effect of a dynamic warm-up versus a proprioceptive neuromuscular facilitation (PNF) warm-up on the sprint and jump performance of recreationally active men. Thirty (n = 30) males were randomly assigned to undergo three sessions of different warm-up types, 72 h apart, involving either proprioceptive neuromuscular facilitation (PNF), dynamic stretching (DS), or no stretching session (control). The PNF and dynamic modes of stretching improved vertical jump performance, F (2.58) = 5.49, p = 0.046, to a certain extent (mean + 3.32% vs. control, p = 0.002 for dynamic and mean + 1.53% vs. control, p = 0.048 for PNF stretching). Dynamic stretching is best used to get a better vertical jump height. Sprint performance was also increased to a greater extent following the stretching session, F (2.58) = 5.60, p = 0.01. Sprint time was +1.05% faster vs. the control, with a value of p = 0.002 after dynamic stretching, while PNF stretching demonstrated a sprint time of +0.35% vs. the control, with a value of p = 0.049. Dynamic stretching showed a better sprint performance and also vertical jump height performance in this study. PNF and dynamic stretching prove to be equally efficacious in flexibility conditioning depending on the type of movement involved. This type of stretching should be utilized to help preserve or improve the performance output of physical activity, especially in sprinting and jumping events.

Analog multiplexing of a laser clock and computational photon counting for fast fluorescence lifetime imaging microscopy.

The dynamic range and fluctuations of fluorescence intensities and lifetimes in biological samples are large, demanding fast, precise, and versatile techniques. Among the high-speed fluorescence lifetime imaging microscopy (FLIM) techniques, directly sampling the output of analog single-photon detectors at GHz rates combined with computational photon counting can handle a larger range of photon rates. Traditionally, the laser clock is not sampled explicitly in fast FLIM; rather the detection is synchronized to the laser clock so that the excitation pulse train can be inferred from the cumulative photon statistics of several pixels. This has two disadvantages for sparse or weakly fluorescent samples: inconsistencies in inferring the laser clock within a frame and inaccuracies in aligning the decay curves from different frames for averaging. The data throughput is also very inefficient in systems with repetition rates much larger than the fluorescence lifetime due to significant silent regions where no photons are expected. We present a method for registering the photon arrival times to the excitation using time-domain multiplexing for fast FLIM. The laser clock is multiplexed with photocurrents into the silent region. Our technique does not add to the existing data bottleneck, has the sub-nanosecond dead time of computational photon counting based fast FLIM, works with various detectors, lasers, and electronics, and eliminates the errors in lifetime estimation in photon-starved conditions. We demonstrate this concept on two multiphoton setups of different laser repetition rates for single and multichannel FLIM multiplexed into a single digitizer channel for real-time imaging of biological samples.

Radiology advocacy: Promoting collaboration between trainees and professional societies.

From mammographic screening guidelines to resident work hour regulations, public policy affects every aspect of the practice of radiology and ultimately determines how radiological care is delivered to patients. Shaping public policy through advocacy is therefore critical to ensure patient access to equitable, high-quality radiological care. In advocacy, individual practicing radiologists and radiology trainees can increase the scope of their influence by collaborating with professional radiology societies. When radiology trainees participate in organized radiology advocacy, they learn about regulatory and legislative issues that will affect their careers, and they learn how to effect policy change. Radiology societies in turn benefit from trainee involvement, as engaging trainees early in their careers leads to more robust future participation and leadership. To encourage trainee involvement, radiology societies can engage individual residency programs and medical student radiology interest groups, invest in trainee-focused events, and maximize the number of positions of responsibility open to trainees. To circumvent the barriers to participation that many trainees face, radiology societies can make meeting proceedings free and available through virtual mediums. Through active collaboration, trainees and professional societies can help assure a bright future for radiologists and patients in need of radiological care.

A comparison of multiple sclerosis disease characteristics across three genetically diverse Asian racial groups in Singapore.

Studies in Western populations have shown that Black and Hispanic patients have an earlier age of Multiple Sclerosis (MS) onset and a more severe disease course characterised by faster disability accrual compared to Whites. It is yet unclear whether MS disease characteristics and clinical course differ amongst Asian racial groups. Singapore is uniquely poised to investigate this as its multi-racial population comprises three genetically diverse Asian racial groups-Chinese, Malay and South Asian. Herein, we sought to elucidate differences in the clinical phenotypes, disease-modifying therapy (DMT) usage, and disease course amongst these three Asian racial groups by performinga retrospective observational study on MS patients seen at the National Neuroscience Institute, Singapore. Data on demographics, disease characteristics, ancillary investigations, and DMT usage were collected. One hundred and eighty-eight patients were included (90 Chinese, 32 Malay, and 66 South Asian). Our findings showed that MS prevalence was the highest in South Asians followed by Malays and Chinese, while demographics, healthcare access, and longer-term disease course were identical across the racial groups. However, several differences and trends were elucidated: (1) South Asian patients had milder sentinel attacks (p = 0.006), (2) a higher proportion of Malay patients had enhancing lesions on their initial MRI (p = 0.057) and the lesion topography differed across the races (p = 0.034), and (3) more Malay patients switched out of their initial DMT (p = 0.051). In conclusion, MS disease characteristics were largely similar across these three Asian racial groups, and while there were some clinical and radiological differences at presentation, these did not influence longer-term outcomes.

The burden of psychiatric morbidity in Multiple Sclerosis, AQP4-antibody NMOSD and MOGAD before and after neurological diagnosis.

BACKGROUND: Psychiatric comorbidities are common in Multiple Sclerosis (MS) and are increasingly recognised in Aquaporin-4-Antibody Neuromyelitis Optica Spectrum Disorders (AQP4-Ab NMOSD) and Myelin Oligodendrocyte Glycoprotein-Antibody Associated Disease (MOGAD). However, it is unclear if these psychiatric comorbidities predate neurological diagnosis or classical neurological symptoms that are conventionally used to establish the onset of these central nervous system inflammatory demyelinating diseases. We sought to: (1) assess the frequency and incidence of psychiatrist-diagnosed psychiatric disorders before and after formal MS, AQP4-Ab NMOSD, and MOGAD diagnosis, and (2) identify potential factors associated with the presence of pre-existing psychiatric morbidity and depression severity at the first clinical visit for MS patients. METHODS: A retrospective observational study was performed on MS, AQP4-Ab NMOSD, and MOGAD patients seen at the National Neuroscience Institute (NNI) Singapore. Individuals with psychiatrist-diagnosed psychiatric disorders before and after neurological diagnosis were identified. Demographic, clinical data, and Patient Health Questionnaire (PHQ)-9 score at first clinic visit were collected and analysed. RESULTS: Three hundred and ninety-nine patients (249 MS, 102 AQP4-Ab NMOSD, 48 MOGAD) were included. A higher proportion of MS patients (13/249, 5.2%) had psychiatric disorders before neurological diagnosis, compared to AQP4-Ab NMOSD (1/102, 1.0%) and MOGAD (0/48, 0.0%) (p = 0.054). Within MS patients, univariate logistic regression revealed that age, sex, race, MS subtype, initial MRI lesion load, and interval between classical MS symptom onset to MS diagnosis were not associated with pre-existing psychiatric disorders. Mean PHQ-9 score for MS patients at their first MS consult was 4.4 (cut-off for no/minimal depression is ≤4); no clinical factors were predictive of higher PHQ-9 scores on univariate linear regression. The proportion of MS patients (29/236, 12.2%) who developed psychiatric illness after neurological diagnosis was not different from AQP4-Ab NMOSD (9/101, 8.9%) (p > 0.999), while this was significantly higher compared to MOGAD (0/48, 0.0%) (p = 0.021). The incidence rate of psychiatric diseases after neurological diagnosis, accounting for follow up time, was also similar between MS and AQP4-Ab NMOSD (incidence rate ratio 1.2; 95% confidence interval 0.54 - 2.8; p = 0.689). CONCLUSION: There is a significant psychiatric burden prior to MS diagnosis compared to AQP4-Ab NMOSD and MOGAD. The increased frequency of psychiatric comorbidity after NMOSD diagnosis merits further study to investigate the determinants of this phenomenon.

DUSP4 modulates RIG-I- and STING-mediated IRF3-type I IFN response.

Detection of cytosolic nucleic acids by pattern recognition receptors, including STING and RIG-I, leads to the activation of multiple signalling pathways that culminate in the production of type I interferons (IFNs) which are vital for host survival during virus infection. In addition to protective immune modulatory functions, type I IFNs are also associated with autoimmune diseases. Hence, it is important to elucidate the mechanisms that govern their expression. In this study, we identified a critical regulatory function of the DUSP4 phosphatase in innate immune signalling. We found that DUSP4 regulates the activation of TBK1 and ERK1/2 in a signalling complex containing DUSP4, TBK1, ERK1/2 and IRF3 to regulate the production of type I IFNs. Mice deficient in DUSP4 were more resistant to infections by both RNA and DNA viruses but more susceptible to malaria parasites. Therefore, our study establishes DUSP4 as a regulator of nucleic acid sensor signalling and sheds light on an important facet of the type I IFN regulatory system.

Multi-actor system dynamics in access to disease-modifying treatments for multiple sclerosis in Southeast Asia: A regional survey and suggestions for improvement.

BACKGROUND: Despite the global availability of multiple sclerosis (MS) treatments, accessing and financing them in Southeast Asia (SEA) remains a challenge. This descriptive survey-based study aimed to describe the current state of MS treatment access and local access dynamics within this region. METHODS: The survey questionnaire, comprising of 15 closed-ended and five open-ended questions, was developed by three neurologists with expertise in MS and routine MS patient management, or had training in neuroimmunology. Questionnaire development was guided by the recent Atlas of MS and in alignment with the Access to Treatment framework, focusing on MS diagnosis and treatment issues in SEA. Fifteen neurologists experienced in managing MS across the region were identified as key informants for this study. RESULTS: All fifteen neurologists participated in the survey via email and videoconferencing between January 2020 and February 2023, which included the following countries: Brunei, Cambodia, Indonesia, Malaysia, Myanmar, Lao PDR, Philippines, Singapore, Thailand, Timor-Leste, and Vietnam. All had at least five years of experience in managing MS patients and six had previously completed a neuroimmunology fellowship programme. SEA countries showed disparities in healthcare financing, availability of neurologists, MS treatments, and investigative tools. Access to MS disease-modifying treatments (DMTs) is hindered by high cost, lack of MS specialists, and weak advocacy efforts. On-label DMTs are not listed as essential medicines regionally except for interferon beta1a and teriflunomide in Malaysia. On-label monoclonals are available only in Malaysia, Singapore, and Thailand. Generic on-label DMTs are unavailable due to lack of distributorship and expertise in using them. Off-label DMTs (azathioprine, methotrexate, and rituximab) predominate in most SEA countries. Other challenges include limited access to investigations, education, and knowledge about DMTs among general neurologists, and absence of registries and MS societies. Patient champions, communities, and MS organisations have limited influence on local governments and pharmaceutical companies. Despite its increasing prevalence, there is a lack of concerted priority setting due to MS being perceived as a rare, non-communicable disease. CONCLUSION: This study highlights the distinct dynamics, challenges, and research gaps within this region, and provides suggestions to improve MS diagnosis, education, and medicine access.

Real-time open-source FLIM analysis.

Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples.