The immunoregulatory landscape of human tuberculosis granulomas.

Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in infected tissues, the architecture and composition of which are thought to affect disease outcome. However, our understanding of the spatial relationships that control human granulomas is limited. Here, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) to image 37 proteins in tissues from patients with active TB. We constructed a comprehensive atlas that maps 19 cell subsets across 8 spatial microenvironments. This atlas shows an IFN-γ-depleted microenvironment enriched for TGF-ß, regulatory T cells and IDO1+ PD-L1+ myeloid cells. In a further transcriptomic meta-analysis of peripheral blood from patients with TB, immunoregulatory trends mirror those identified by granuloma imaging. Notably, PD-L1 expression is associated with progression to active TB and treatment response. These data indicate that in TB granulomas, there are local spatially coordinated immunoregulatory programs with systemic manifestations that define active TB.

DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .

Use of neurological criteria to declare death in children.

Accurate determination of death is a necessary responsibility of the medical profession. Brain death, or death by neurological criteria (DNC), can be legally declared after the determination of permanent loss of clinical brain function, including the capacity for consciousness, brainstem reflexes, and the ability to breathe spontaneously. Despite longstanding debates over the exact definition of brain death or DNC and how it is determined, most middle- and high-income countries have compatible medical protocols and legal policies for brain death or DNC. This review summarizes the 2023 updated guidelines for brain death or DNC determination, which integrate adult and pediatric diagnostic criteria. We discuss the clinical challenges related to brain death or DNC determination in infants and young children. We emphasize that physicians must follow the standardized and meticulous evaluation processes outlined in these guidelines to reduce diagnostic error and ensure no false positive determinations. An essential component of the brain death or DNC evaluation is appropriate and transparent communication with families. Ongoing efforts to promote consistency and legal uniformity in the declaration of death are needed.

Fetal anomaly diagnosis and termination of pregnancy.

The aim of this review was to discuss bioethics in prenatal diagnosis and health care after recent legislative and judicial changes affecting reproductive rights, such as the repeal of 'Roe v. Wade' in the United States. We recognize that abortion involves particular moralities that are not universal or shared by all cultures, groups, and individuals. We reviewed the historical aspects of embryology and personhood, fetal morbidity and mortality, and parental options for prenatal diagnostic testing. We examined relevant ethical issues including informed consent, the emergence of fetal pain, reproductive autonomy, the fiduciary responsibilities of pregnant mothers, and the obligations of physicians caring for the maternal-fetal dyad. The code of medical ethics includes respect for decisional privacy and the protection of information shared in confidence. When a fetal anomaly is diagnosed, pregnant mothers must be informed about the risks, burdens, and alternatives in either continuing or terminating the pregnancy. Parental choice should include the right to refuse testing, the informed choice not to know about certain genetic test results, and the right to make informed decisions about the best interests of the future child. In the diagnosis and care of fetal anomalies, moral dilemmas arise. Before fetal viability, the mother's autonomy, sense of beneficence, and personal values should be trusted and respected. Perinatal palliative care should be available to pregnant mothers whose anomalous fetus is carried to birth.

Delineation of a novel neurodevelopmental syndrome associated with PAX5 haploinsufficiency.

PAX5 is a transcription factor associated with abnormal posterior midbrain and cerebellum development in mice. PAX5 is highly loss-of-function intolerant and missense constrained, and has been identified as a candidate gene for autism spectrum disorder (ASD). We describe 16 individuals from 12 families who carry deletions involving PAX5 and surrounding genes, de novo frameshift variants that are likely to trigger nonsense-mediated mRNA decay, a rare stop-gain variant, or missense variants that affect conserved amino acid residues. Four of these individuals were published previously but without detailed clinical descriptions. All these individuals have been diagnosed with one or more neurodevelopmental phenotypes including delayed developmental milestones (DD), intellectual disability (ID), and/or ASD. Seizures were documented in four individuals. No recurrent patterns of brain magnetic resonance imaging (MRI) findings, structural birth defects, or dysmorphic features were observed. Our findings suggest that PAX5 haploinsufficiency causes a neurodevelopmental disorder whose cardinal features include DD, variable ID, and/or ASD.

Deep learning for cellular image analysis.

Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.

Predominant and novel de novo variants in 29 individuals with ALG13 deficiency: Clinical description, biomarker status, biochemical analysis, and treatment suggestions.

Asparagine-linked glycosylation 13 homolog (ALG13) encodes a nonredundant, highly conserved, X-linked uridine diphosphate (UDP)-N-acetylglucosaminyltransferase required for the synthesis of lipid linked oligosaccharide precursor and proper N-linked glycosylation. De novo variants in ALG13 underlie a form of early infantile epileptic encephalopathy known as EIEE36, but given its essential role in glycosylation, it is also considered a congenital disorder of glycosylation (CDG), ALG13-CDG. Twenty-four previously reported ALG13-CDG cases had de novo variants, but surprisingly, unlike most forms of CDG, ALG13-CDG did not show the anticipated glycosylation defects, typically detected by altered transferrin glycosylation. Structural homology modeling of two recurrent de novo variants, p.A81T and p.N107S, suggests both are likely to impact the function of ALG13. Using a corresponding ALG13-deficient yeast strain, we show that expressing yeast ALG13 with either of the highly conserved hotspot variants rescues the observed growth defect, but not its glycosylation abnormality. We present molecular and clinical data on 29 previously unreported individuals with de novo variants in ALG13. This more than doubles the number of known cases. A key finding is that a vast majority of the individuals presents with West syndrome, a feature shared with other CDG types. Among these, the initial epileptic spasms best responded to adrenocorticotropic hormone or prednisolone, while clobazam and felbamate showed promise for continued epilepsy treatment. A ketogenic diet seems to play an important role in the treatment of these individuals.

Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development.

Global expansion of hydropower resources has increased in recent years to meet growing energy demands and fill worldwide gaps in electricity supply. However, hydropower induces significant environmental impacts on river ecosystems - impacts that are addressed through environmental impact assessment (EIA) processes. The need for effective EIA processes is increasing as environmental regulations are either stressed in developing countries undertaking rapid expansion of hydropower capacity or time- and resource-intensive in developed countries. Part of the challenge in implementing EIAs lies in reaching a consensus among stakeholders regarding the most important environmental factors as the focus of impact studies. To help address this gap, we developed a weight-of-evidence approach (and toolkit) as a preliminary and coarse assessment of the most relevant impacts of hydropower on primary components of the river ecosystem, as identified using river function indicators. Through a science-based questionnaire and predictive model, users identify which environmental indicators may be impacted during hydropower development as well as those indicators that have the highest levels of uncertainty and require further investigation. Furthermore, an assessment tool visualizes inter-dependent indicator relationships, which help formulate hypotheses about causal relationships explored through environmental studies. We apply these tools to four existing hydropower projects and one hypothetical new hydropower project of varying sizes and environmental contexts. We observed consistencies between the output of our tools and the Federal Energy Regulatory Commission licensing process (inclusive of EIAs) but also important differences arising from holistic scientific evaluations (our toolkit) versus regulatory policies. The tools presented herein are aimed at increasing the efficiency of the EIA processes that engender environmental studies without loss of rigor or transparency of rationale necessary for understanding, considering, and mitigating the environmental consequences of hydropower.

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually-tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from MERFSIH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.

Reclaiming freshwater sustainability in the Cadillac Desert.

Increasing human appropriation of freshwater resources presents a tangible limit to the sustainability of cities, agriculture, and ecosystems in the western United States. Marc Reisner tackles this theme in his 1986 classic Cadillac Desert: The American West and Its Disappearing Water. Reisner's analysis paints a portrait of region-wide hydrologic dysfunction in the western United States, suggesting that the storage capacity of reservoirs will be impaired by sediment infilling, croplands will be rendered infertile by salt, and water scarcity will pit growing desert cities against agribusiness in the face of dwindling water resources. Here we evaluate these claims using the best available data and scientific tools. Our analysis provides strong scientific support for many of Reisner's claims, except the notion that reservoir storage is imminently threatened by sediment. More broadly, we estimate that the equivalent of nearly 76% of streamflow in the Cadillac Desert region is currently appropriated by humans, and this figure could rise to nearly 86% under a doubling of the region's population. Thus, Reisner's incisive journalism led him to the same conclusions as those rendered by copious data, modern scientific tools, and the application of a more genuine scientific method. We close with a prospectus for reclaiming freshwater sustainability in the Cadillac Desert, including a suite of recommendations for reducing region-wide human appropriation of streamflow to a target level of 60%.

Genomic disorders on chromosome 22.

PURPOSE OF REVIEW: Chromosome 22, the first human chromosome to be completely sequenced, is prone to genomic alterations. Copy-number variants (CNVs) are common because of an enrichment of low-copy repeat sequences that precipitate a high frequency of nonallelic homologous misalignments and unequal recombination during meiosis. Among these is one of the most common multiple anomaly syndromes in humans and the most common microdeletion syndrome, velocardiofacial syndrome (VCFS), also known as 22q11.2 deletion syndrome and DiGeorge syndrome. This review will focus on the recent literature dealing with both the molecular and clinical aspects of chromosome 22 genomic variations. Although the literature covering this area is expansive, the majority is descriptive or analytical of the problems presented by these genomic disorders, and there is little evidence of translational research including treatment outcomes. RECENT FINDINGS: With the increased use of microarray analysis in both research and clinical practice, variations in CNVs are becoming elucidated. Genomic analysis continues to characterize genes and gene effect. Research on the COMT gene continues to yield interesting findings, including a possible sex-mediated effect because of its regulatory role with estrogen. There is a small amount of treatment outcome data relevant to neuropsychiatric disorders in VCFS, but based on small samples and short-term follow-up. SUMMARY: Although hundreds of studies in the past year have focused on genomic disorders of chromosome 22, little progress has been made in the implementation of translational research, even for more common disorders including VCFS.

Semaphorin-Plexin Signaling: From Axonal Guidance to a New X-Linked Intellectual Disability Syndrome.

BACKGROUND: Semaphorins and plexins are ligands and cell surface receptors that regulate multiple neurodevelopmental processes such as axonal growth and guidance. PLXNA3 is a plexin gene located on the X chromosome that encodes the most widely expressed plexin receptor in fetal brain, plexin-A3. Plexin-A3 knockout mice demonstrate its role in semaphorin signaling in vivo. The clinical manifestations of semaphorin/plexin neurodevelopmental disorders have been less widely explored. This study describes the neurological and neurodevelopmental phenotypes of boys with maternally inherited hemizygous PLXNA3 variants. METHODS: Data-sharing through GeneDx and GeneMatcher allowed identification of individuals with autism or intellectual disabilities (autism/ID) and hemizygous PLXNA3 variants in collaboration with their physicians and genetic counselors, who completed questionnaires about their patients. In silico analyses predicted pathogenicity for each PLXNA3 variant. RESULTS: We assessed 14 boys (mean age, 10.7 [range 2 to 25] years) with maternally inherited hemizygous PLXNA3 variants and autism/ID ranging from mild to severe. Other findings included fine motor dyspraxia (92%), attention-deficit/hyperactivity traits, and aggressive behaviors (63%). Six patients (43%) had seizures. Thirteen boys (93%) with PLXNA3 variants showed novel or very low allele frequencies and probable damaging/disease-causing pathogenicity in one or more predictors. We found a genotype-phenotype correlation between PLXNA3 cytoplasmic domain variants (exons 22 to 32) and more severe neurodevelopmental disorder phenotypes (P < 0.05). CONCLUSIONS: We report 14 boys with maternally inherited, hemizygous PLXNA3 variants and a range of neurodevelopmental disorders suggesting a novel X-linked intellectual disability syndrome. Greater understanding of PLXNA3 variant pathogenicity in humans will require additional clinical, computational, and experimental validation.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

Ethical Perspectives on Costly Drugs and Health Care: AAN Position Statement.

High drug prices have created substantial challenges for patients, physicians, health systems, and payers. High drug prices can affect patient care in many ways, including limiting access to treatment, increasing the burden of administrative tasks, and contributing to physician burnout. Exorbitant drug pricing poses direct challenges for distributive justice, which is concerned with fairly distributing benefits and burdens across society. In this position statement, we discuss ethical concerns raised by high drug costs, primarily focusing on concerns around distributive justice. We consider forms of rationing, approaches to allocation, potential complexities in real-life application, and structural forces contributing to high drug costs. Finally, we consider potential policy solutions and ramifications for individual clinicians.