Fourteen quick tips for crowdsourcing geographically linked data for public health advocacy.

This article presents 14 quick tips to build a team to crowdsource data for public health advocacy. It includes tips around team building and logistics, infrastructure setup, media and industry outreach, and project wrap-up and archival for posterity.

Multi-scale systems genomics analysis predicts pathways, cell types, and drug targets involved in normative variation in peri-adolescent human cognition.

An open challenge in human genetics is to better understand the systems-level impact of genotype variation on developmental cognition. To characterize the genetic underpinnings of peri-adolescent cognition, we performed genotype-phenotype and systems analysis for binarized accuracy in nine cognitive tasks from the Philadelphia Neurodevelopmental Cohort (~2,200 individuals of European continental ancestry aged 8-21 years). We report a region of genome-wide significance within the 3' end of the Fibulin-1 gene (P = 4.6 × 10-8), associated with accuracy in nonverbal reasoning, a heritable form of complex reasoning ability. Diffusion tensor imaging data from a subset of these participants identified a significant association of white matter fractional anisotropy with FBLN1 genotypes (P < 0.025); poor performers show an increase in the C and A allele for rs77601382 and rs5765534, respectively, which is associated with increased fractional anisotropy. Integration of published human brain-specific 'omic maps, including single-cell transcriptomes of the developing human brain, shows that FBLN1 demonstrates greatest expression in the fetal brain, as a marker of intermediate progenitor cells, demonstrates negligible expression in the adolescent and adult human brain, and demonstrates increased expression in the brain in schizophrenia. Collectively these findings warrant further study of this gene and genetic locus in cognition, neurodevelopment, and disease. Separately, genotype-pathway analysis identified an enrichment of variants associated with working memory accuracy in pathways related to development and to autonomic nervous system dysfunction. Top-ranking pathway genes include those genetically associated with diseases with working memory deficits, such as schizophrenia and Parkinson's disease. This work advances the "molecules-to-behavior" view of cognition and provides a framework for using systems-level organization of data for other biomedical domains.

Genotyping SARS-CoV-2 Variants Using Ratiometric Nucleic Acid Barcode Panels.

Designing diagnostic assays to genotype rapidly mutating viruses remains a challenge despite the overall improvements in nucleic acid detection technologies. RT-PCR and next-generation sequencing are unsuitable for genotyping during outbreaks or in point-of-care detection due to their infrastructure requirements and longer turnaround times. We developed a quantum dot barcode multiplexing system to genotype mutated viruses. We designed multiple quantum dot barcodes to target conserved, wildtype, and mutated regions of SARS-CoV-2. We calculated ratios of the signal output from different barcodes that enabled SARS-CoV-2 detection and identified SARS-CoV-2 variant strains from a sample. We detected different sequence types, including conserved genes, nucleotide deletions, and single nucleotide substitutions. Our system detected SARS-CoV-2 patient specimens with 98% sensitivity and 94% specificity across 91 patient samples. Further, we leveraged our barcoding and ratio system to track the emergence of the N501Y SARS-CoV-2 mutation from December 2020 to May 2021 and demonstrated that the more transmissible N501Y mutation started to dominate infections by April 2021. Our barcoding and signal ratio approach can genotype viruses and track the emergence of viral mutations in a single diagnostic test. This technology can be extended to tracking other viruses. Combined with smartphone detection technologies, this assay can be adapted for point-of-care tracking of viral mutations in real time.

Green synthesized hydroxyapatite nanoadsorbent for the adsorptive removal of AB113 dye for environmental applications.

The present work reports the synthesis of hydroxyapatite (HAp) via the green chemistry approach by using the leaf extract of copper pod tree and its adsorptive potential to remove Acid blue 113 (AB113) dye. FESEM-EDS characterization of the synthesized HAp confirmed rod-shaped HAp with prominent Ca and P elements. The crystallinity of HAp was ascertained by XRD and thermal stability was analyzed by TGA. The colloidal suspension stability was determined as - 17.7 mV by Zeta potential analyzer. The mesoporous structure was affirmed via BET studies with a high magnitude of specific surface area. TEM studies substantiated the rod-shaped HAp as observed in FESEM. The signals specific to HAp were observed in XPS studies. Adsorption of AB113 on the synthesized HAp was examined by varying the process parameters. Batch experiments resulted in an optimum dye removal of 92.72% at a pH of 8, 1 g/L of CP-HAp nps dosage, 20 ppm AB113 concentration, 120 min contact time, 150 rpm agitation speed and at room temperature. The maximum adsorption capacity reached 120.48 mg/g. Multifarious isotherms characterized the adsorption with Freundlich isotherm (R2 > 0.968) dominating Langmuir indicating multilayer adsorption. The experimental data reasonably matched pseudo-second-order kinetics with R2 exceeding 0.99. Thermodynamic investigations underlined the spontaneity and exothermicity of the processes. Results showed the suitability of the HAp nanoadsorbent to remove AB113 from wastestreams.

Adsorptive removal of AB113 dye using green synthesized hydroxyapatite/magnetite nanocomposite.

In the present study, magnetite nanoparticles (Fe3O4NPs) synthesized using Thunbergia grandiflora leaf extract as a reducing agent were doped with hydroxyapatite sourced from waste bivalve clamshells to produce hydroxyapatite/magnetite nanocomposite (HA/Fe3O4NPs). The magnetic nanocomposite was examined using several characterization techniques. The results of XRD and FESEM, analysis showed HA/Fe3O4NPs have a crystalline phase and irregular spherical particles respectively. EDAX and FTIR confirmed the presence of specific elements and functional groups of both iron oxide and hydroxyapatite nanoparticles respectively. The surface area and superparamagnetic property of the composite were determined by BET and VSM analysis. Central Composite Design (CCD) was used to optimize the adsorption process to remove of AB113 from aqueous solutions. The optimal adsorption efficiency was found out to be 94.38% at pH 8, AB113 dye concentration 54 ppm, HA/Fe3O4NPs dose 84 mg, and an agitation speed of 174 rpm. The monolayer Langmuir isotherm was the best model with a sorption capacity of 109.98 mg/g which was higher than the reported values. The pseudo-second-order kinetic model displayed a good fit with an R2 = 0.99. Thermodynamic parameters were assessed which confirmed the exothermic adsorption process. Therefore, the synthesized magnetic nanocomposite can be employed as a novel nanoadsorbent for the removal of anionic dyes from waste effluents.

Network Approaches for Precision Oncology.

The growth of multi-omic tumour profile datasets along with knowledge of genome regulatory networks has created an unprecedented opportunity to advance precision oncology. Achieving this goal requires computational methods that can make sense of and combine heterogeneous data sources. Interpretability and integration of prior knowledge is of particular relevance for genomic models to minimize ungeneralizable models, promote rational treatment design, and make use of sparse genetic mutation data. While networks have long been used to capture genomic interactions at the levels of genes, proteins, and pathways, the use of networks in precision oncology is relatively new. In this chapter, I provide an introduction to network-based approaches used to integrate multi-modal data sources for patient stratification and patient classification. There is a particular emphasis on methods using patient similarity networks (PSNs) as part of the design. I separately discuss strategies for inferring driver mutations from individual patient mutation data. Finally, I discuss challenges and opportunities the field will need to overcome to achieve its full potential, with an outlook towards a clinic of the future.

netDx: interpretable patient classification using integrated patient similarity networks.

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine-learning approaches across most cancer types. Compared to traditional machine-learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.

Epigenetics of major psychosis: progress, problems and perspectives.

Understanding the origins of normal and pathological behavior is one of the most exciting opportunities in contemporary biomedical research. There is increasing evidence that, in addition to DNA sequence and the environment, epigenetic modifications of DNA and histone proteins may contribute to complex phenotypes. Inherited and/or acquired epigenetic factors are partially stable and have regulatory roles in numerous genomic activities, thus making epigenetics a promising research path in etiological studies of psychiatric disease. In this article, we review recent epigenetic studies examining the brain and other tissues, including those from individuals with schizophrenia (SCZ) and bipolar disorder (BPD). We also highlight heuristic aspects of the epigenetic theory of psychiatric disease and discuss the future directions of psychiatric epigenetics.

Metformin reduces the clonal fitness of Dnmt3aR878H hematopoietic stem and progenitor cells by reversing their aberrant metabolic and epigenetic state.

Clonal hematopoiesis (CH) arises when a hematopoietic stem cell (HSC) acquires a mutation that confers a competitive advantage over wild-type (WT) HSCs, resulting in its clonal expansion. Individuals with CH are at an increased risk of developing hematologic neoplasms and a range of age-related inflammatory illnesses1-3. Therapeutic interventions that suppress the expansion of mutant HSCs have the potential to prevent these CH-related illnesses; however, such interventions have not yet been identified. The most common CH driver mutations are in the DNA methyltransferase 3 alpha (DNMT3A) gene with arginine 882 (R882) being a mutation hotspot. Here we show that murine hematopoietic stem and progenitor cells (HSPCs) carrying the Dnmt3aR878H/+ mutation, which is equivalent to human DNMT3AR882H/+, have increased mitochondrial respiration compared with WT cells and are dependent on this metabolic reprogramming for their competitive advantage. Importantly, treatment with metformin, an oral anti-diabetic drug with inhibitory activity against complex I in the electron transport chain (ETC), reduced the fitness of Dnmt3aR878H/+ HSCs. Through a multi-omics approach, we discovered that metformin acts by enhancing the methylation potential in Dnmt3aR878H/+ HSPCs and reversing their aberrant DNA CpG methylation and histone H3K27 trimethylation (H3K27me3) profiles. Metformin also reduced the fitness of human DNMT3AR882H HSPCs generated by prime editing. Our findings provide preclinical rationale for investigating metformin as a preventive intervention against illnesses associated with DNMT3AR882 mutation-driven CH in humans.

Mesoporous calcium hydroxide nanoparticle synthesis from waste bivalve clamshells and evaluation of its adsorptive potential for the removal of Acid Blue 113 dye.

Calcium hydroxide nanoadsorbent was prepared from waste bivalve clamshells and used for the adsorptive removal of Acid Blue 113 (AB113) dye. The morphology, elemental nature, functional groups, and thermal stability of the nanoadsorbent were characterized by various methods. The nanoadsorbent had a high monolayer adsorption capacity (153.53 mg/g) for AB113 dye. Langmuir and Temkin isotherms better fitted (R2 > 0.95) the experimental data. The adsorption rate followed pseudo-second-order kinetics (R2 > 0.99). The thermodynamic study ascertained spontaneous and exothermic adsorption. This study confirmed the possibility of using calcium hydroxide as an adsorbent to effectively remove AB113 dye from aqueous solutions.

Comparative Study of Clinical Severity and Biochemical Markers in Pre COVID-19 and COVID-19 Rhino-Orbito Cerebral Mucormycosis.

In view of high surge of sinonasal mucormycosis cases after the second wave of covid 19, present study was planned to know and compare the clinical severity of the disease and also to better understand the difference in the biochemical markers during precovid and post covid period. This retrospective observational study included all cases of sinonasal mucormycosis which were treated in our institute from August 2012 to August 2021. Details of these cases were collected from hospital database system. Biochemical parameters included FBS, HbA1C, urine ketone bodies, blood pH and creatinine. Clinical severity score was measured using self-structured severity scoring system. We found that out of 74 cases treated in our hospital 28 cases were in pre covid period while 46 cases belonged to covid 19 period. Higher male predominance was seen during post covid period (76% vs. 60%). Urine ketone bodies were positive in 7% patients in precovid period compared to 26% in post-covid period. FBS and HbA1C were high approximately 80 and 90% patients respectively in both groups. Clinical severity was significantly high in post covid patients. The present study showed that in spite of similar biochemical profile. The severity of mucormycosis was high in covid positive patients. This study shows that Covid-19 is an independent high risk factor in mucormycosis patients.

COVID-19 cluster size and transmission rates in schools from crowdsourced case reports.

The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter ß, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.

During the COVID-19 pandemic, public health officials promoted social distancing as a way to reduce SARS-CoV-2 transmission. The goal of social distancing is to reduce the number, proximity, and duration of face-to-face interactions between people. To achieve this, people shifted many activities online or canceled events outright. In education, some schools closed and shifted to online learning, while others continued classes in person with safety precautions. Better information about SARS-CoV-2 transmission in schools could help public health officials to make decisions of what activities to keep in person and when to suspend classes. If safety measures lower transmission in schools considerably, then closing schools may not be worth online education's social, educational, and economic costs. However, if transmission of SARS-CoV-2 in schools remains high despite measures, closing schools may be essential, despite the costs. Tupper et al. used data about COVID-19 cases in children attending in-person school in four Canadian provinces between 2020 and 2021 to fit a computer model of school transmission. On average, their analysis shows that one infected person in a school leads to between two and three further cases. Most of the time, no more students are infected, indicating that normally infection clusters are small; and only rarely does one infected person set off a large outbreak. The model also showed that measures to reduce transmission, like masking or small class sizes, were more effective than interventions such as keeping students with the same cohort all day (bubbling). Tupper et al. caution that their findings apply to the variants of SARS-CoV-2 circulating in Canada during the 2020-2021 school year, and may not apply to newer, highly transmissible strains like Omicron. However, the model could always be adapted to assess school or workplace transmission of more recent strains of SARS-CoV-2, and more generally of other diseases. Thus, Tupper et al. provide a new approach to estimating the rate of disease transmission and comparing the impact of different prevention strategies.

Magnetic activated charcoal/Fe2O3 nanocomposite for the adsorptive removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions: Synthesis, characterization, optimization, kinetic and isotherm studies.

Magnetic activated charcoal/Fe2O3 nanocomposite (AC/Fe2O3NC) was fabricated using Spondias dulcis leaf extract by a facile method and used for the adsorptive removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions for the first time. The nanocomposite was characterized by methods such as FE-SEM, EDS, XRD, FTIR, TGA, VSM, and BET to identify and confirm the surface morphology, elemental composition, crystalline nature, functional groups, thermal stability, magnetic behavior, and surface area respectively. Box-Behnken Design (BBD) - an optimization method, which belongs to the Response surface methodology (RSM) and a modeling tool - Artificial Neural Network (ANN) were employed to design, optimize and predict the relationship between the input parameters (pH, initial concentration of 2,4-D, time and agitation speed) versus the output parameter (adsorption efficiency of 2,4-D). Adsorption efficiency of 98.12% was obtained at optimum conditions (pH: 2.05, initial concentration: 32 ppm, contact time: 100 min, agitation speed: 130 rpm, temperature: 30 °C, and dosage: 0.2 g/L). The predictive ability of the ANN was superior (R2 = 0.99) than the quadratic model, given by the RSM (R2 = 0.93). The equilibrium data were best-fitted to Langmuir isotherm (R2 = 0.9944) and the kinetics obeyed pseudo-second-order model (R2 = 0.9993) satisfactorily. Thermodynamic studies revealed the spontaneity and exothermic nature of adsorption. The maximum adsorption capacity, qm was found to be 255.10 mg/g, substantially larger than the reported values for 2,4-D adsorption by other magnetic nanoadsorbents. Therefore, this nanoadsorbent may be utilized as an excellent alternative for the elimination of 2,4-D from the waterbodies.

A recent update on green synthesized iron and iron oxide nanoparticles for environmental applications.

Nanotechnology is considered the budding discipline in various fields of science and technology. In this review, the various synthesis methods of iron and iron oxide nanoparticles were summarised with more emphasis on green synthesis - a sustainable and eco-friendly method. The mechanism of green synthesis of these nanomaterials was reviewed in recent literature. The magnetic properties of these nanomaterials were briefed which makes them unique in the family of nanomaterials. An overview of various removal methods for the pollutants such as dye, heavy metals, and emerging contaminants using green synthesized iron and iron oxide nanoparticles is discussed. The mechanism of pollutant removal methods like Fenton-like degradation, photocatalytic degradation, and adsorption techniques was also detailed. The review is concluded with the challenges and possible future aspects of these nanomaterials for various environmental applications.

Superparamagnetic hematite spheroids synthesis, characterization, and catalytic activity.

The leaf extract of Muntingia calabura is being first reported to be used for the synthesis superparamagnetic hematite nanoparticles by following the green-chemistry approach. Field Emission - Scanning Electron Microscopic image revealed the formation of irregular nano spheroids averaging at 48.57 nm in size and characteristic of Fe and O atoms, as revealed by Energy Dispersive X-Ray spectrum. X-ray diffraction analysis results proved the crystallinity of hematite diffraction planes with crystallite sizes averaging at 30.68 nm. The lattice parameter values stayed concordant with the literature. The superparamagnetic nature was attested by the high value of saturation magnetism (2.20 emu/g) with negligible coercivity and retentivity. Fourier Transform Infrared Spectroscopy results affirmed numerous moieties involved in the synthesis of hematite nanoparticles and the existence of signature Fe-O bands. Thermogravimetric analysis studies portrayed the thermal behavior nanoparticles with 28% weight loss and thermal stability was attained after 700 °C. X-ray photoelectron spectroscopy analysis confirmed the valence states of Fe and O in the hematite nanoparticles and ascertained the purity. The mesoscopic structure was revealed by Brunauer-Emmett-Teller studies with considerable surface area (112.50 m2/g). The Fenton-like catalysis mediated by the nanoparticle sample was demonstrated by degrading methylene blue dye. The remarkable degradation efficiency of 93.44% was obtained and the kinetics was conformed to a second-order model with a high R2 value. Therefore, the highly crystalline and mesoporous superparamagnetic hematite spheroids prepared using the leaf extract of M. calabura would find promising applications in various catalysis processes.

As (III) removal using superparamagnetic magnetite nanoparticles synthesized using Ulva prolifera - optimization, isotherm, kinetic and equilibrium studies.

In this study, magnetite nanoparticles (MNPs) were synthesized using the seaweed - Ulva prolifera, an amply found marine source in the Western coastal regions of India. The surface and other properties of MNPs were characterized by many sophisticated methods. Spherical nanoclusters were observed in the FESEM image and iron and oxygen elements were seen in EDS results. XRD peaks were consistent with magnetite standards and MNPs had good crystallinity. FTIR portrayed the specific signals for MNPs and TGA profile ascertained the thermal stability. Magnetic saturation of 41.84 emu/g with negligible hysteresis loop substantiated the superparamagnetism. XPS pointed out the presence of Fe and O with oxidation states specific for MNPs, and the results were consistent with EDS. BET revealed a high specific surface area (144.98 m2/g) of MNPs with mesopores. The synthesized MNPs were used as nanoadsorbent for the removal of As (III) from aqueous solution. The central composite design was used for optimizing As (III) adsorption on MNPs. The optimum conditions were found out as 97.5% at pH: 9, rotation speed: 150 rpm, time: 90 min, and MNPs dosage: 1.15 g/L. The adsorption process fitted in a better way with the Langmuir isotherm and pseudo-second-order model. The highest adsorption capacity was 12.45 mg/g, which is substantially larger than the documenter reports. The spontaneous and endothermic nature of adsorption were ascertained from thermodynamic studies. The results suggested that the synthesized MNPs using the extract of U. prolifera could be alternative nanoadsorbents for eliminating toxic heavy metals from waste streams.

Adsorptive removal of Acid Blue 113 using hydroxyapatite nanoadsorbents synthesized using Peltophorum pterocarpum pod extract.

The present work reports the study on the green synthesis of hydroxyapatite (HAP) nanoadsorbents using Peltophorum pterocarpum pod extract. HAP nanoadsorbents were characterized by using FESEM, EDS, TEM, XRD, FTIR, XPS, and BET analyses. The results highlighted the high purity, needle-like aggregations, and crystalline nature of the prepared HAP nanoadsorbents. The surface area was determined as 40.04 m2/g possessing mesopores that can be related to the high adsorption efficiency of the HAP for the removal of a toxic dye, - Acid Blue 113 (AB 113) from water. Central Composite Design (CCD) was used for optimizing the adsorption process, which yielded 94.59% removal efficiency at the optimum conditions (dose: 0.5 g/L, AB 113 dye concentration: 25 ppm, agitation speed: 173 rpm, and adsorption time: 120 min). The adsorption kinetics followed the pseudo-second-order model (R2:0.9996) and the equilibrium data fitted well with the Freundlich isotherm (R2:0.9924). The thermodynamic parameters indicated that the adsorption of AB 113 was a spontaneous and exothermic process. The highest adsorption capacity was determined as 153.85 mg/g, which suggested the promising role of green HAP nanoadsorbents in environmental remediation applications.

A review on adsorptive removal of dyes from wastewater by hydroxyapatite nanocomposites.

Dye removal from wastewater is of prominence due to its hostile effects on human health and the environment. The complex structure of the dye molecule is responsible for its difficulty in removal. Adsorption is found to be a promising technique to eliminate dye wastes due to its high removal capacity at low concentration. Among different adsorbents used, hydroxyapatite is a biocompatible adsorbent that is relatively efficient in both anionic and cationic dye removal. Recently, modification of hydroxyapatite by doping with other materials to increase its removal efficiency has gained much attention. This review summarizes compilation of recent literature on the removal of anionic and cationic dye by different hydroxyapatite nanocomposites, comparison of adsorption capacities of different hydroxyapatite nanocomposites, the possible adsorption mechanism of removal of dyes, the general isotherm, and kinetic and thermodynamic studies explaining the type of adsorption and the characteristics, advantages, and limitations of adsorbents.

Recovery of value-added products from wastewater using Aqueous Two-Phase Systems - A review.

Aqueous two-phase system (ATPS) is long seen as a technique that promotes higher purity and yield in a single step. It is witnessing increased acceptance as a preferred choice for combined goals of concentration, separation and purification of a target product, be it industrially valuable or environmentally contaminating. Purification of biomolecules like enzymes, proteins, nucleic acids, viruses, etc. has been the forte of ATPS. Currently, the technique is used for concentrating the toxic fractions from diverse industrial let offs, from food, dairy, beverage, pharmaceuticals, agriculture, dyeing, tannery and metal-processing industries. Apart from being simple, efficient, rapid, flexible, economical, and biocompatible, the selectivity, purity and yield are on par and sometimes higher than the traditional downstream steps. From an industrial angle too, problems related to scale-up of ATPS are being actively addressed. Many novel approaches are being added by way of varying ATPS components to increase the yield and purity. Another case in point is the inclusion of optimization techniques for zeroing in on the precise setting of the operating parameters. With increasing impetus to the approach, we attempt to draw attention from academia and industries, alike, that are developing novel tweaks to the currently existing practices in ATPS. This review aims to assess and evaluate the different types of ATPS that have been used for the recovery of valuables and contaminants from industrial waste discharges.

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data - a common problem in real-world data - without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.