Quantified trends in provider reimbursement and utilization volume in colorectal cancer screenings: analysis of Medicare claims from 2000 to 2019.

AIM: Colorectal cancer (CRC) screening rates in the United States remain persistently below guideline targets, partly due to suboptimal patient utilization and provider reimbursement. To guide long-term national utilization estimates and set reasonable screening adherence targets, this study aimed to quantify trends in utilization of and reimbursement for CRC screenings using Medicare claims. METHOD: Inflation-adjusted reimbursements and utilization volume associated with each CRC screening code were abstracted from Medicare claims between 2000 and 2019. Screenings, screenings/100 000 enrolees and reimbursement/screening were analysed with linear regression and compared with the equality of slopes tests. Average reimbursement per screening was compared using analysis of variance with Dunnett's T3 multiple comparisons test. RESULTS: The growth rate of multitarget stool DNA tests (mt-sDNA)/100 000 was the highest at 170.4 screenings/year (R2 = 0.99, p ≤ 0.001), while that of faecal occult blood tests/100 000 was the lowest at -446.4 screenings/year (R2 = 0.90, p ≤ 0.001) (p ≤ 0.001). Provider reimbursements averaged $546.95 (95% CI $520.12-$573.78) per mt-sDNA screening, significantly higher than reimbursements for all invasive screenings. Only FOBTs significantly increased in reimbursement per screening at $0.62/year (R2 = 0.91, p ≤ 0.001). CONCLUSION: We derived forecastable trend numbers for utilization and provider reimbursement. Faecal immunochemical tests/100 000 and mt-sDNA screenings/100 000 increased most rapidly during the entire study period. The number of nearly all invasive screenings/100 000 decreased rapidly; the number of colonoscopies/100 000 increased slightly, probably due to superior diagnostic strength. These trends indicate the that replacement of other invasive modalities with accessible noninvasive screenings will account for much of future screening behaviour and thus reductions in CRC incidence and mortality, especially given providers' reimbursement incentive to screen average-risk patients with stool-based tests.

Helicobacter pylori Burden in the United States According to Individual Demographics and Geography: A Nationwide Analysis of the Veterans Healthcare System.

BACKGROUND & AIMS: There are no contemporary large-scale studies evaluating the burden of Helicobacter pylori in the United States according to detailed demographics. The primary objective was to evaluate H pylori positivity in a large national healthcare system according to individual demographics and geography. METHODS: We conducted a nationwide retrospective analysis of adults in the Veterans Health Administration who completed H pylori testing between 1999 and 2018. The primary outcome was H pylori positivity overall, as well as according to zip code-level geography, race, ethnicity, age, sex, and time period. RESULTS: Among 913,328 individuals (mean, 58.1 years; 90.2% male) included between 1999 and 2018, H pylori was diagnosed in 25.8%. Positivity was highest in non-Hispanic black (median, 40.2%; 95% confidence interval [CI], 40.0%-40.5%) and Hispanic (36.7%; 95% CI, 36.4%-37.1%) individuals and lowest in non-Hispanic white individuals (20.1%; 95% CI, 20.0%-20.2%). Although H pylori positivity declined in all racial and ethnic groups over the timeframe, the disproportionate burden of H pylori in non-Hispanic black and Hispanic compared with non-Hispanic white individuals persisted. Approximately 4.7% of the variation in H pylori positivity was explained by demographics, with race and ethnicity accounting for the vast majority. CONCLUSIONS: The burden of H pylori is substantial in the United States among veterans. These data should (1) motivate research aimed at better understanding why marked demographic differences in H pylori burden persist so that mitigating interventions may be implemented and (2) guide resource allocation to optimize H pylori testing and eradication in high-risk groups.

Applying the RE-AIM framework to evaluate an educational model to "close the gap" and improve health equity in uncontrolled hypertension.

OBJECTIVES: To use the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework to evaluate an educational model addressing self-management of uncontrolled hypertension. METHODS: We used a pre-post single cohort design to identify minoritized patients with uncontrolled hypertension (systolic > 160 mmHg and/or diastolic > 100 mmHg). Trained Ambassadors provided telephone outreach, skill-based blood pressure (BP) monitoring, and goal-setting for 4 months. Follow-up occurred at 7 months. We evaluated the initiative using the RE-AIM framework and quantitative analysis for process and outcome measures post-intervention. RESULTS: Among Black patients (n = 345), the average age was 55.4 years (8.7), half identified as male (n = 173, 50.1 %); many were uninsured (n = 159, 46.1 %). Engagement in calls occurred for 67.8 % (n = 234) of the cohort; monitor distribution was 22.9 % (n = 79); and goal setting occurred for 64 patients. BP improved for 40 % of the cohort (mean pre: 168/98 mmHg, mean post: 150/89 mmHg; p < 0.0001) and 40 % of patients' last known BP was < 140/90. CONCLUSIONS: RE-AIM evaluation of the Closing the Gap initiative suggests that the model is associated with BP reduction in high-risk Black patients. PRACTICE IMPLICATIONS: An educational model for patient-centered hypertension management in low-income populations is feasible and addresses self-monitoring barriers.

Delayed presentation of traumatic supra- and infratentorial extradural hematoma: illustrative case.

BACKGROUND: Supra- and infratentorial epidural hematomas (SIEDHs) are a rare subtype of epidural hematoma (EDH), showing expanding bleeding on both sides of the tentorium, and account for <2% of EDHs (Aji, Apriawan, and Bajamal, 2018). These lesions can typically expand and decompensate quickly, making immediate diagnosis and surgical intervention crucial. OBSERVATIONS: The authors' patient presented >48 hours from a blunt trauma to the right side of the head with progressive vomiting and bruising behind the right ear. He had a Glasgow Coma Scale score of 15 on arrival. Head computed tomography showed an SIEDH measuring approximately 3 cm, and, given the hematoma's size and mass effect, the patient was taken emergently to the operating room for decompression, where the source of bleeding was noted to be an emissary vein from the transverse sinus. The linear parietooccipital fracture was mended with mesh cranioplasty. Patient imaging and follow-up showed an excellent recovery. LESSONS: Although SIEDH is rare, patients can present in a delayed fashion and be neurologically intact. The threshold to obtain imaging to rule out delayed hemorrhage should be low in any patient with a history of trauma in the region of a dural venous sinus.

Systemic Racism Affecting Latinx Population Health During the COVID-19 Pandemic and Beyond: Perspectives of Latinx Community Health Workers and Community-Based Organization Leaders.

Introduction: The purpose of this study is to identify forms of systemic racism experienced by Latinx communities in North Carolina during the COVID-19 pandemic as identified by Latinx community health workers (CHWs) and community-based organization (CBO) leaders. Methods: We conducted three focus groups in July 2022 (N=16). We performed qualitative analysis of data using an iterative inductive approach of the original language in Dedoose. Results: Four central themes emerged: (1) Access to resources for Latinx individuals; (2) Immediate, transitional, and future fears; (3) Benefits of CHWs; and (4) Lessons learned. Discussion: Institutional and state policies often do not involve community members, such as CHWs and CBO leaders, at the start of the development process, leading to ineffective interventions that perpetuate health disparities and systemic racism. Health Equity Implications: Community-informed policy recommendations can improve alignment of community and policy priorities to create more effective interventions to address systemic racism and promote health equity.

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme.

Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.

Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR-formatted genomic variants.

Introduction: Variant annotation is a critical component in next-generation sequencing, enabling a sequencing lab to comb through a sea of variants in order to hone in on those likely to be most significant, and providing clinicians with necessary context for decision-making. But with the rapid evolution of genomics knowledge, reported annotations can quickly become out-of-date. Under the ONC Sync for Genes program, our team sought to standardize the sharing of dynamically annotated variants (e.g., variants annotated on demand, based on current knowledge). The computable biomedical knowledge artifacts that were developed enable a clinical decision support (CDS) application to surface up-to-date annotations to clinicians. Methods: The work reported in this article relies on the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) Genomics and Global Alliance for Genomics and Health (GA4GH) Variant Annotation (VA) standards. We developed a CDS pipeline that dynamically annotates patient's variants through an intersection with current knowledge and serves up the FHIR-encoded variants and annotations (diagnostic and therapeutic implications, molecular consequences, population allele frequencies) via FHIR Genomics Operations. ClinVar, CIViC, and PharmGKB were used as knowledge sources, encoded as per the GA4GH VA specification. Results: Primary public artifacts from this project include a GitHub repository with all source code, a Swagger interface that allows anyone to visualize and interact with the code using only a web browser, and a backend database where all (synthetic and anonymized) patient data and knowledge are housed. Conclusions: We found that variant annotation varies in complexity based on the variant type, and that various bioinformatics strategies can greatly improve automated annotation fidelity. More importantly, we demonstrated the feasibility of an ecosystem where genomic knowledge bases have standardized knowledge (e.g., based on the GA4GH VA spec), and CDS applications can dynamically leverage that knowledge to provide real-time decision support, based on current knowledge, to clinicians at the point of care.

Automated HL7v2 LRI informatics framework for streamlining genomics-EHR data integration.

While VCF formatted files are the lingua franca of next-generation sequencing, most EHRs do not provide native VCF support. As a result, labs often must send non-structured PDF reports to the EHR. On the other hand, while FHIR adoption is growing, most EHRs support HL7 interoperability standards, particularly those based on the HL7 Version 2 (HL7v2) standard. The HL7 Version 2 genomics component of the HL7 Laboratory Results Interface (HL7v2 LRI) standard specifies a formalism for the structured communication of genomic data from lab to EHR. We previously described an open-source tool (vcf2fhir) that converts VCF files into HL7 FHIR format. In this report, we describe how the utility has been extended to output HL7v2 LRI data that contains both variants and variant annotations (e.g., predicted phenotypes and therapeutic implications). Using this HL7v2 converter, we implemented an automated pipeline for moving structured genomic data from the clinical laboratory to EHR. We developed an open source hl7v2GenomicsExtractor that converts genomic interpretation report files into a series of HL7v2 observations conformant to HL7v2 LRI. We further enhanced the converter to produce output conformant to Epic's genomic import specification and to support alternative input formats. An automated pipeline for pushing standards-based structured genomic data directly into the EHR was successfully implemented, where genetic variant data and the clinical annotations are now both available to be viewed in the EHR through Epic's genomics module. Issues encountered in the development and deployment of the HL7v2 converter primarily revolved around data variability issues, primarily lack of a standardized representation of data elements within various genomic interpretation report files. The technical implementation of a HL7v2 message transformation to feed genomic variant and clinical annotation data into an EHR has been successful. In addition to genetic variant data, the implementation described here releases the valuable asset of clinically relevant genomic annotations provided by labs from static PDFs to calculable, structured data in EHR systems.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease.

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Dissecting the Relationship Between Neuropsychiatric and Neurodegenerative Disorders.

Neurodegenerative diseases (NDDs) and neuropsychiatric disorders (NPDs) are two common causes of death in elderly people, which includes progressive neuronal cell death and behavioral changes. NDDs include Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, and motor neuron disease, characterized by cognitive defects and memory impairment, whereas NPDs include depression, seizures, migraine headaches, eating disorders, addictions, palsies, major depressive disorders, anxiety, and schizophrenia, characterized by behavioral changes. Mounting evidence demonstrated that NDDs and NPDs share an overlapping mechanism, which includes post-translational modifications, the microbiota-gut-brain axis, and signaling events. Mounting evidence demonstrated that various drug molecules, namely, natural compounds, repurposed drugs, multitarget directed ligands, and RNAs, have been potentially implemented as therapeutic agents against NDDs and NPDs. Herein, we highlighted the overlapping mechanism, the role of anxiety/stress-releasing factors, cytosol-to-nucleus signaling, and the microbiota-gut-brain axis in the pathophysiology of NDDs and NPDs. We summarize the therapeutic application of natural compounds, repurposed drugs, and multitarget-directed ligands as therapeutic agents. Lastly, we briefly described the application of RNA interferences as therapeutic agents in the pathogenesis of NDDs and NPDs. Neurodegenerative diseases and neuropsychiatric diseases both share a common signaling molecule and molecular phenomenon, namely, pro-inflammatory cytokines, γCaMKII and MAPK/ERK, chemokine receptors, BBB permeability, and the gut-microbiota-brain axis. Studies have demonstrated that any alterations in the signaling mentioned above molecules and molecular phenomena lead to the pathophysiology of neurodegenerative diseases, namely, Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis, and neuropsychiatric disorders, such as bipolar disorder, schizophrenia, depression, anxiety, autism spectrum disorder, and post-traumatic stress disorder.

Long-term effect of seasonal and constant low temperatures on mesophilic biomass treating sewage in continuously stirred tank anaerobic granular reactor.

A Continuously Stirred Tank Anaerobic Granular Reactor seeded with mesophilic biomass was studied for 1733 days analysing the impact of seasonal (12-23 °C) and controlled (8-15 °C) low temperatures on anaerobic treatment of sewage. Aided by intermittent dosing of 0.04% (v/v) methanol, the microbiota quickly adapted to temperature fluctuations. Chemical oxygen demand (COD) removal efficiency was high but low temperatures affected methane production. Under low-temperature stress, the Methanomythylovorans and Methanosaeta-dominated methanogenic community shifted focus to cellular repair and transport, with carbon diversion towards assimilative pathways, thereby decreasing methane yields. Specific methanogenic activity at 15 °C and 30 °C increased by five and four times, respectively, from their initial values indicating microbiota retained its mesophilic properties. Despite lower methane yield, stable and high COD removals, along with low dissolved methane and volatile fatty acids indicated that low-temperature anaerobic sewage treatment using mesophilic biomass in the long run is sustainable.

A versatile laser-induced porcine model of outer retinal and choroidal degeneration for preclinical testing.

Over 30 million people worldwide suffer from untreatable vision loss and blindness associated with childhood-onset and age-related eye diseases caused by photoreceptor (PR), retinal pigment epithelium (RPE), and choriocapillaris (CC) degeneration. Recent work suggests that RPE-based cell therapy may slow down vision loss in late stages of age-related macular degeneration (AMD), a polygenic disease induced by RPE atrophy. However, accelerated development of effective cell therapies is hampered by the lack of large-animal models that allow testing safety and efficacy of clinical doses covering the human macula (20 mm2). We developed a versatile pig model to mimic different types and stages of retinal degeneration. Using an adjustable power micropulse laser, we generated varying degrees of RPE, PR, and CC damage and confirmed the damage by longitudinal analysis of clinically relevant outcomes, including analyses by adaptive optics and optical coherence tomography/angiography, along with automated image analysis. By imparting a tunable yet targeted damage to the porcine CC and visual streak - with a structure similar to the human macula - this model is optimal for testing cell and gene therapies for outer retinal diseases including AMD, retinitis pigmentosa, Stargardt, and choroideremia. The amenability of this model to clinically relevant imaging outcomes will facilitate faster translation to patients.

Multiple therapeutic approaches of glioblastoma multiforme: From terminal to therapy.

Glioblastoma multiforme (GBM) is an aggressive brain cancer showing poor prognosis. Currently, treatment methods of GBM are limited with adverse outcomes and low survival rate. Thus, advancements in the treatment of GBM are of utmost importance, which can be achieved in recent decades. However, despite aggressive initial treatment, most patients develop recurrent diseases, and the overall survival rate of patients is impossible to achieve. Currently, researchers across the globe target signaling events along with tumor microenvironment (TME) through different drug molecules to inhibit the progression of GBM, but clinically they failed to demonstrate much success. Herein, we discuss the therapeutic targets and signaling cascades along with the role of the organoids model in GBM research. Moreover, we systematically review the traditional and emerging therapeutic strategies in GBM. In addition, we discuss the implications of nanotechnologies, AI, and combinatorial approach to enhance GBM therapeutics.

Celebrating 50 years of microbial granulation technologies: From canonical wastewater management to bio-product recovery.

Microbial granulation technologies (MGT) in wastewater management are widely practised for more than fifty years. MGT can be considered a fine example of human innovativeness-driven nature wherein the manmade forces applied during operational controls in the biological process of wastewater treatment drive the microbial communities to modify their biofilms into granules. Mankind, over the past half a century, has been refining the knowledge of triggering biofilm into granules with some definite success. This review captures the journey of MGT from inception to maturation providing meaningful insights into the process development of MGT-based wastewater management. The full-scale application of MGT-based wastewater management is discussed with an understanding of functional microbial interactions within the granule. The molecular mechanism of granulation through the secretion of extracellular polymeric substances (EPS) and signal molecules is also highlighted in detail. The recent research interest in the recovery of useful bioproducts from the granular EPS is also emphasized.

Unwinding the modalities of necrosome activation and necroptosis machinery in neurological diseases.

Necroptosis, a regulated form of cell death, is involved in the genesis and development of various life-threatening diseases, including cancer, neurological disorders, cardiac myopathy, and diabetes. Necroptosis initiates with the formation and activation of a necrosome complex, which consists of RIPK1, RIPK2, RIPK3, and MLKL. Emerging studies has demonstrated the regulation of the necroptosis cell death pathway through the implication of numerous post-translational modifications, namely ubiquitination, acetylation, methylation, SUMOylation, hydroxylation, and others. In addition, the negative regulation of the necroptosis pathway has been shown to interfere with brain homeostasis through the regulation of axonal degeneration, mitochondrial dynamics, lysosomal defects, and inflammatory response. Necroptosis is controlled by the activity and expression of signaling molecules, namely VEGF/VEGFR, PI3K/Akt/GSK-3ß, c-Jun N-terminal kinases (JNK), ERK/MAPK, and Wnt/ß-catenin. Herein, we briefly discussed the implication and potential of necrosome activation in the pathogenesis and progression of neurological manifestations, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, traumatic brain injury, and others. Further, we present a detailed picture of natural compounds, micro-RNAs, and chemical compounds as therapeutic agents for treating neurological manifestations.

Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration.

OBJECTIVE: Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications. MATERIALS AND METHODS: FHIR Genomics Operations essentially "wrap" a genomics data repository, presenting a uniform interface to applications. More importantly, operations encapsulate the complexity of data within a repository and normalize redundant data representations-particularly relevant in genomics, where a tremendous amount of raw data exists in often-complex non-FHIR formats. RESULTS: Fifteen FHIR Genomics Operations have been developed, designed to support a wide range of clinical scenarios, such as variant discovery; clinical trial matching; hereditary condition and pharmacogenomic screening; and variant reanalysis. Operations are being matured through the HL7 balloting process, connectathons, pilots, and the HL7 FHIR Accelerator program. DISCUSSION: Next-generation sequencing can identify thousands to millions of variants, whose clinical significance can change over time as our knowledge evolves. To manage such a large volume of dynamic and complex data, new models of genomics-EHR integration are needed. Qualitative observations to date suggest that freeing application developers from the need to understand the nuances of genomic data, and instead base applications on standardized APIs can not only accelerate integration but also dramatically expand the applications of Omic data in driving precision care at scale for all.

Uneven terrain treadmill walking in younger and older adults.

We developed a method for altering terrain unevenness on a treadmill to study gait kinematics. Terrain consisted of rigid polyurethane disks (12.7 cm diameter, 1.3-3.8 cm tall) which attached to the treadmill belt using hook-and-loop fasteners. Here, we tested four terrain unevenness conditions: Flat, Low, Medium, and High. The main objective was to test the hypothesis that increasing the unevenness of the terrain would result in greater gait kinematic variability. Seventeen younger adults (age 20-40 years), 25 higher-functioning older adults (age 65+ years), and 29 lower-functioning older adults (age 65+ years, Short Physical Performance Battery score < 10) participated. We customized the treadmill speed to each participant's walking ability, keeping the speed constant across all four terrain conditions. Participants completed two 3-minute walking trials per condition. Using an inertial measurement unit placed over the sacrum and pressure sensors in the shoes, we calculated the stride-to-stride variability in step duration and sacral excursion (coefficient of variation; standard deviation expressed as percentage of the mean). Participants also self-reported their perceived stability for each condition. Terrain was a significant predictor of step duration variability, which roughly doubled from Flat to High terrain for all participant groups: younger adults (Flat 4.0%, High 8.2%), higher-functioning older adults (Flat 5.0%, High 8.9%), lower-functioning older adults (Flat 7.0%, High 14.1%). Similarly, all groups exhibited significant increases in sacral excursion variability for the Medium and High uneven terrain conditions, compared to Flat. Participants were also significantly more likely to report feeling less stable walking over all three uneven terrain conditions compared to Flat. These findings support the hypothesis that altering terrain unevenness on a treadmill will increase gait kinematic variability and reduce perceived stability in younger and older adults.

Creative destruction: New protein folds from old.

Mechanisms of emergence and divergence of protein folds pose central questions in biological sciences. Incremental mutation and stepwise adaptation explain relationships between topologically similar protein folds. However, the universe of folds is diverse and riotous, suggesting more potent and creative forces are at play. Sequence and structure similarity are observed between distinct folds, indicating that proteins with distinct folds may share common ancestry. We found evidence of common ancestry between three distinct ß-barrel folds: Scr kinase family homology (SH3), oligonucleotide/oligosaccharide-binding (OB), and cradle loop barrel (CLB). The data suggest a mechanism of fold evolution that interconverts SH3, OB, and CLB. This mechanism, which we call creative destruction, can be generalized to explain many examples of fold evolution including circular permutation. In creative destruction, an open reading frame duplicates or otherwise merges with another to produce a fused polypeptide. A merger forces two ancestral domains into a new sequence and spatial context. The fused polypeptide can explore folding landscapes that are inaccessible to either of the independent ancestral domains. However, the folding landscapes of the fused polypeptide are not fully independent of those of the ancestral domains. Creative destruction is thus partially conservative; a daughter fold inherits some motifs from ancestral folds. After merger and refolding, adaptive processes such as mutation and loss of extraneous segments optimize the new daughter fold. This model has application in disease states characterized by genetic instability. Fused proteins observed in cancer cells are likely to experience remodeled folding landscapes and realize altered folds, conferring new or altered functions.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis.

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Dental Operating Microscope-guided Retrieval of Broken Instrument from a Deciduous Molar Using Ultrasonics.

Endodontic procedures are associated with various mishaps, one of which is instrument breakage. It can act as an obstruction to mechanical and chemical cleaning of an infected root canal, thereby hampering the prognosis of treatment. Instrument retrieval must be performed with minimum damage to a tooth and surrounding tissues, minimal loss of radicular dentine and simultaneously maintaining the original canal shape as much as possible. This case report represents successful retrieval of a separated K file fragment from the distobuccal canal of the right mandibular primary second molar 85 with ultrasonic energy application under the dental operating microscope. How to cite this article: Kaul R, Gupta R, Chhabra S, et al. Dental Operating Microscope-guided Retrieval of Broken Instrument from a Deciduous Molar Using Ultrasonics. Int J Clin Pediatr Dent 2022;15(S-1):S114-S118.