Search Results (2,479)

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background: Anti-synthetase Syndrome (ASyS) is an idiopathic inflammatory myopathy characterized by muscle weakness and inflammatory infiltrates in muscles. Sjogren’s disease (SD) is an autoimmune condition primarily affecting exocrine glands. Both these conditions may present lung involvement. We describe a female patient with anti-synthetase/SD overlap syndrome and review the literature to identify published cases describing this overlap, aiming to better define its clinical, radiological, and serological features. Methods: The case description was based on a retrospective collection of clinical, laboratory, and imaging data related to the patient’s diagnostic process and clinical course. Data were anonymized and handled in accordance with the competent territorial Ethics Committee. A literature review was performed using the MEDLINE and Scopus databases by combining the keywords “Anti-Synthetase syndrome”, “Sjögren disease”, “Sjögren syndrome”, “Myositis”, and “Interstitial lung disease” (ILD). Published cases were selected if they met the 2016 EULAR/ACR criteria for SD and at least one of the currently proposed classification criteria for ASyS. Results: The described case concerns a 68-year-old woman with rapidly progressive ILD. The diagnosis of anti-synthetase/SD overlap syndrome was based on clinical, serological (anti-Ro52 and anti-PL7 antibodies), histological, and radiological findings. Despite immunosuppressive and antifibrotic treatment, the clinical course worsened, leading to a poor outcome. In addition, six relevant cases were identified in the literature. Clinical presentations, autoantibody profiles, radiological findings, and outcomes were highly heterogeneous. Among the reported cases, no standardized treatment protocols were adopted, reflecting the lack of consensus in managing this rare condition. Conclusions: In anti-synthetase/SD overlap syndrome, ILD may follow a rapidly progressive course. Early recognition can be challenging, especially in the absence of muscular involvement. This case-based review highlights the need for more standardized approaches to the diagnosis and management of this rare and complex overlap syndrome. Full article

Background: Core laboratory chemistry analyzers typically use plasma and serum samples, while blood gas instruments use whole blood for electrolyte and metabolite tests. Due to high costs to back up the core lab chemistry analyzers, especially in the remote small community hospitals, we have verified the interchangeability of serum/plasma electrolytes and metabolites on blood gas instruments (GEM4000 and Radiometer ABL90) vs. chemistry analyzers. In this study, we sought to extend the investigation to another blood gas device—Radiometer ABL837. Methods: One plasma separator tube and one serum separator tube were drawn from 20 apparently healthy individuals and outpatients and 20 intensive care unit patients. All the samples were run on Roche Cobas8000, and then were run on three Radiometer ABL837 analyzers for sodium (Na⁺), potassium (K⁺), chloride (Cl⁻), glucose, lactate (plasma only), and creatinine parameters. Paired measurements between the ABL837 and Cobas8000 were compared, and their difference were assessed for statistical and clinical significance. Results: ABL837 demonstrated statistical significance (p < 0.05) vs. Cobas8000 on all the plasma and serum parameters. However, no parameter differences were found when comparing the plasma/serum results on ABL837 to those on Cobas8000, indicating that none were clinically significant. ABL837 also demonstrated good–excellent correlations with Cobas8000 on all the parameters. Conclusions: When comparing metabolite and electrolyte values with plasma and serum sample types, the ABL837 blood gas instruments and Cobas 8000 chemistry analyzer are interchangeable. These data proves that ABL837 can be used as a backup for a chemistry analyzer in measuring plasma and serum electrolyte and metabolite concentrations. Full article

Objectives: Burkholderia cepacia complex (Bcc), a Gram-negative organism, is a well-recognized cause of hospital outbreaks, often linked to a contaminated shared source, such as multidose medications. In this study, we report an outbreak of Bcc infections in a tertiary care hospital, associated with the intrinsic contamination of a prepared solution used in interventional radiology (IR) procedures. Additionally, we provide a detailed explanation of the interventions implemented to control and interrupt the outbreak. Methods: Records from the infection control committee from 1 January 2023 to 31 October 2024 were screened to identify cases with Bcc growth in cultured blood, urine, or respiratory samples. Clinical and laboratory data were collected in March 2025. Bacterial identification was performed using conventional methods and MALDI-TOF (Bruker Daltonics, Bremen, Germany). Controls were matched to cases by ward, date of initial growth, and duration of hospitalization. Demographic and clinical data of these patients were systematically collected and analyzed. Microbiological cultures were obtained from environmental objects of concern and certain medications. Results: A total of 82 Burkholderia species were identified. We enrolled 77 cases and 77 matched controls. The source of contamination was identified in ready-to-use intravenous medications (remifentanil and magnesium preparations) in the IR department. These preparations were compounded in advance by the team and were used repeatedly. Although the outbreak originated from contaminated IV medications used in IR, secondary transmission likely affected 28 non-IR patients via fomites, shared environments, and possible lapses in isolation precautions. The mortality rate among the cases was 16.9%. Infection with Bcc was associated with prolonged intensive care unit stays (p = 0.018) and an extended overall hospitalization duration (p < 0.001); however, it was not associated with increased mortality. The enforcement of contact precautions and comprehensive environmental decontamination successfully reduced the incidence of the Bcc outbreak. No pathogens were detected in cultures obtained after the disinfection. Conclusions: The hospital transmission of Bcc is likely driven by cross-contamination, invasive medical procedures, and the administration of contaminated medications. Implementing stringent infection control measures such as staff retraining, updated policies on medication use, enhanced environmental decontamination, and strict adherence to isolation precautions has proven effective in curbing the spread of virulent and transmissible Bcc. Full article

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

Invasive infections with rare yeasts are increasing worldwide and are associated with higher mortality rates due to their resistance to antifungal drugs. Accurate antifungal susceptibility testing (AFST) is crucial for proper management of rare yeast infections. We performed AFST of 74 Candida kefyr isolates by Etest, EUCAST-based MICRONAUT-AM assay (MCN-AM) and reference Clinical and Laboratory Standards Institute broth microdilution method (CLSI). Essential agreement (EA, ±1 two-fold dilution), categorical agreement (CA), major errors (MEs) and very-major errors (VmEs) were determined using epidemiological cut-off values of ≤1.0 µg/mL, ≤0.03 µg/mL, ≤0.5 µg/mL and ≤1 µg/mL, defining wild-type isolates for fluconazole, voriconazole, micafungin and amphotericin B (AMB), respectively. Results for AMB susceptibility were correlated with ERG2/ERG3 mutations and total-cell sterols. CA of ≥97% was recorded between any two methods while EA varied between 72 and 82%, 87 and 92%, and 49 and 76% for fluconazole, voriconazole and micafungin, respectively. For AMB, CAs between CLSI and Etest; CLSI and MCN-AM; MCN-AM and Etest were 95% (4 ME, 0 VmE), 96% (3 ME, 0 VmE) and 99%, respectively, while EA varied from 32% to 69%. Non-synonymous ERG2/ERG3 mutations and no ergosterol were found in seven of eight isolates of non-wild types for AMB by Etest. Our data show that Etest, CLSI and MCN-AM methods are suitable for AFST of C. kefyr for fluconazole, voriconazole and micafungin. Excellent CAs for AMB between Etest and MCN-AM with concordant sterol profiles but not with CLSI suggest that Etest is also an excellent alternative for the detection of C. kefyr isolates with reduced susceptibility to AMB. Full article

Currently, due to the complexity of obtaining samples, specific features of laboratory processing and analysis of the results, there is a lack of data on the microbial signature of the small intestine in healthy and diseased states of the upper gastrointestinal tract. Objective: To investigate the characteristics of the small intestinal microbiome in acute pancreatitis of varying severity and to identify correlations with clinical factors. Methods: This study included 30 patients with acute pancreatitis of varying severity treated between 1 January 2019 and 31 December 2021. The composition of the microbiota was analyzed by metagenomic sequencing of the 16S rRNA gene from jejunal samples. Results: The mortality rate in the study group was 23.3%. The small intestinal microbiome was dominated by Streptococcus (median relative abundance 19.2%, interquartile range 6.4–35.1%), Veillonella (3.4%; 0.6–7%), Granulicatella (2.7%; 0.6–5%), Fusobacterium (2.2%; 0.3–5.9%), Prevotella (1.5%; 0.3–8%), Haemophilus (0.9%; 0.2–10%), Gemella (0.8%; 0.2–4.3%), and Lactobacillus (0.2%; 0.1–0.9%). More severe disease was associated with decreased abundance of Neisseria mucosa, Parvimonas micra, and Megasphaera micronuciformis. In contrast, the relative abundance of the genera Streptococcus (species S. rubneri/parasanguinis/australis), Actinomyces, and several genera within the family Enterobacteriaceae was higher in these patients. Conclusions: The state of the microbiota has important prognostic value and correlates with the duration from the onset of the pain syndrome to the time of receiving qualified care in the hospital. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

Background: A critical gap exists in the current literature regarding pediatric-specific creatinine reference data. This study established age- and sex-stratified reference intervals and a corresponding median (Qcr) model for serum creatinine in children, providing a crucial foundation for improved diagnostic accuracy and clinical decision-making in this vulnerable population. Methods: A total of 9090 children (52.38% males and 47.65% females) who were getting regular check-ups at clinical laboratories in three regions were included in this study to establish Qcr serum and reference ranges for creatinine concentration. Results: The reference values and serum Qcr creatinine were established for children based on age and sex. Both males and females experience an incremental increase in creatinine levels with advancing age. In addition, significant differences were seen across the three areas in other age groups (p < 0.05). Conclusions: These newly established, age- and sex-stratified reference and Qcr values provide a critical resource for clinical laboratories, empowering clinicians to more accurately assess pediatric renal function and enabling more precise, individualized care for children with renal concerns. Full article

Background/Objective: Candidemia is a serious complication following intensive chemotherapy and is associated with high mortality in pediatric patients. This study aimed to identify the factors associated with 28-day mortality in pediatric patients with candidemia. Methods: We retrospectively analyzed 63 pediatric patients diagnosed with acute leukemia and candidemia following intensive chemotherapy. Clinical characteristics, laboratory findings, and epidemiological data were collected. Antifungal susceptibility data were available for 60 patients. Kaplan–Meier survival analysis was used to estimate the 28-day mortality rate, and Cox regression was performed to identify prognostic factors. Results: The 28-day mortality rate among the 63 patients (57.1% male, median age 9.74 years) was 36.5%. Candida tropicalis was the predominant species (96.8%). Antifungal susceptibility rates were 100% for amphotericin B and caspofungin and 22.2% for fluconazole. The factors independently associated with reduced 28-day mortality were an absolute lymphocyte count (ALC) ≥ 0.2 G/L at the time of candidemia diagnosis (5.3% vs. 50% mortality; hazard ratio [HR] = 0.08; 95% confidence interval [CI], 0.01–0.61), the use of antifungal prophylaxis (AFP) (26.3% vs. 52%; HR 0.31; 95% CI, 0.13–0.74), and granulocyte transfusion (GTX) combined with granulocyte colony-stimulating factor (G-CSF) (20% vs. 47.4%; HR = 0.31; 95% CI, 0.11–0.85). Conclusions: Our findings suggest that an ALC ≥ 0.2 G/L, AFP, and the administration of a GTX combined with G-CSF may be considered favorable prognostic factors. Full article

Background and Aims: Sepsis-associated acute kidney injury (S-AKI) is common and is associated with poor outcomes. This prospective observational study aimed to assess the predictive value of four novel biomarkers—syndecan-1 (SDC1), neutrophil gelatinase-associated lipocalin (NGAL), proenkephalin (PENK), and presepsin (PSPN)—for renal outcomes and mortality in septic ICU patients. Methods: Serum biomarker levels were measured in serum samples collected at the time of sepsis diagnosis on the basis of the Sepsis-3 criteria. Acute kidney injury (AKI) was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, and patients were grouped by the presence of AKI, renal replacement therapy requirement (RRT), and intensive care unit (ICU) survival. Demographic, clinical, laboratory, and severity score data were compared between groups to evaluate the predictive performance of biomarkers and clinical parameters. Results: Of the 140 septic patients included, 55.0% developed AKI, 17.2% required RRT, and the ICU mortality rate was 50.0%. SDC1 was independently associated with both AKI (OR: 1.201; p = 0.024) and RRT initiation (OR: 1.260; p = 0.004). It also demonstrated the highest predictive performance for RRT (AUC: 0.715; p = 0.001) and a significant AUC for AKI evaluation (AUC: 0.659; p = 0.002). NGAL levels were significantly elevated in patients with AKI and higher SOFA scores but were not independently predictive. PENK and PSPN were not significantly associated with any renal outcome or mortality. The combined SOFA–SDC1 model improved discrimination for both AKI (AUC: 0.770) and RRT (AUC: 0.737), surpassing individual predictors. Conclusions: SDC1 emerged as the most reliable biomarker for assessing AKI and predicting the need for RRT, highlighting its potential role in early renal risk stratification among critically ill patients. Full article

Background and objective. Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) affects small- to medium-sized vessels and is characterized by the production of ANCAs. The ANCA-negative term is used if the patient otherwise fulfills the definition for AAV but has negative results on serologic testing for ANCAs. The objective of this study was to compare ANCA-positive and -negative vasculitis patients and to evaluate the main differences possibly related to the presence of ANCAs. Material and methods. A cross-sectional study of 73 patients treated at the tertiary Rheumatology Centre of University Hospital from the 1 January, 2001, to the 31August, 2023, with diagnoses of AAV was carried out. Clinical characteristics and laboratory data were collected at the onset or at the first year of the disease. Results. Forty-eight (65.8%) patients were ANCA-positive, while twenty-five (34.3%) were ANCA-negative. Distribution by gender was similar in both groups, with a female–male ratio of 2:1. C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were elevated for all AAV patients, but values were higher in the ANCA-positive patients’ group. The median hemoglobin was 106 g/L in the seropositive group and 127 g/L in the seronegative group. A higher prevalence of kidney involvement (60.4%) with elevated serum creatinine level (93.5 µmol/L) was observed in the ANCA-positive group compared with 24% and 70 µmol/l in the ANCA-negative group (p < 0.05). Neurological involvement was more frequently found in the ANCA-positive patient group, too: 29.2% compared to 20%. Among patients with ANCA-negative vasculitis, 88% had pulmonary; 92% ear, nose, throat (ENT); 48% joint; and 28% skin presentation. In comparison, involvement of these organs was less common in the ANCA-positive patients’ group, at 79.2%, 60.4%, 31.3%, and 25 %, respectively. Conclusions. ANCA-positive patients appear to be in a more difficult clinical situation in terms of organ involvement and laboratory changes. Full article

Background and Objectives: Ischemic stroke (IS) is a critical health issue, affecting individuals of all ages, sexes, and backgrounds. Mounting evidence suggests that sex indeed could play some distinct role in shaping the incidence, outcomes, and treatment of IS. In the context of the COVID-19 pandemic, contradictory findings from previous studies that also addressed sex differences in cerebrovascular diseases demonstrate the need for further focused research. This study aimed to evaluate the sex discrepancies in the clinical presentation of IS and its outcomes in patients admitted to Kaunas Hospital of the Lithuanian University of Health Sciences (LUHS), Lithuania. Materials and Methods: This is a retrospective record-based single-center study. All the study patients—727 men and 1082 women—enrolled between 1 January 2020, and 27 February 2022; suffered from acute IS; and had absolute contraindications against interventional IS treatment. These patients received a conservative non-interventional IS treatment at the neurological department of the LUHS’s Kaunas Hospital. The sociodemographic data; laboratory findings; comorbidities, including COVID-19; in-hospital complications; and outcome factors were obtained from the patients’ medical records and evaluated by deploying appropriate statistical tests. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by the Cox proportional hazards regression for in-hospital lethality. Results: The mean age of IS patients was significantly higher in women compared to men (p < 0.001), as was the proportion of in-hospital deaths (19.10% and 15.36%, respectively; p < 0.05). The mean total number of in-hospital complications was again significantly higher in the group of women compared to men (p < 0.05). The prevalence of COVID-19 was higher in men compared to women (p < 0.05). COVID-19 diagnosis (HR = 1.53; p = 0.02) and acute in-hospital pulmonary complications (HR = 1.91; p = 0.008) significantly increased the risk of in-hospital lethality in men. The risk of in-hospital lethality was significantly higher in women with comorbid diabetes mellitus type 2 (DM) compared to those with comorbid isolated arterial hypertension (AH) (HR = 2.25, p = 0.007). Increased C-reactive protein elevated the risk of in-hospital lethality by more than twice in both men and women (HR = 2.46; p < 0.001 and HR = 2.28; p < 0.001, respectively). Conclusions: The following differences between men and women with IS were determined: Acute in-hospital pulmonary complications, including COVID-19, significantly increased the risk of in-hospital lethality in the male group, but not in women. However, women suffering from DM had a significantly increased risk of in-hospital lethality compared with those women IS patients with AH or chronic ischemic heart disease (IHD). Increased C-reactive protein was associated with an elevated risk of in-hospital lethality both in male and female groups. Full article

Parkinson’s disease (PD), a progressive neurodegenerative disorder, imposes growing clinical and socioeconomic burdens worldwide. Despite landmark discoveries in dopamine biology and α-synuclein pathology, translating mechanistic insights into effective, personalized interventions remains elusive. Recent advances in molecular profiling, neuroimaging, and computational modeling have broadened the understanding of PD as a multifactorial systems disorder rather than a purely dopaminergic condition. However, critical gaps persist in diagnostic precision, biomarker standardization, and the translation of bench side findings into clinically meaningful therapies. This review critically examines the current landscape of PD research, identifying conceptual blind spots and methodological shortfalls across pathophysiology, clinical evaluation, trial design, and translational readiness. By synthesizing evidence from molecular neuroscience, data science, and global health, the review proposes strategic directions to recalibrate the research agenda toward precision neurology. Here I highlight the urgent need for interdisciplinary, globally inclusive, and biomarker-driven frameworks to overcome the fragmented progression of PD research. Grounded in the Accelerating Medicines Partnership-Parkinson’s Disease (AMP-PD) and the Parkinson’s Progression Markers Initiative (PPMI), this review maps shared biomarkers, open data, and patient-driven tools to faster personalized treatment. In doing so, it offers actionable insights for researchers, clinicians, and policymakers working at the intersection of biology, technology, and healthcare delivery. As the field pivots from symptomatic relief to disease modification, the road forward must be cohesive, collaborative, and rigorously translational, ensuring that laboratory discoveries systematically progress to clinical application. Full article