Recent advances in proteomic-based diagnostics of cystic fibrosis.

INTRODUCTION: Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF. AREAS COVERED: We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers. EXPERT OPINION: Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery.

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography-mass spectrometry (LC-MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Dystrophin Protein Quantification as a Duchenne Muscular Dystrophy Diagnostic Biomarker in Dried Blood Spots Using Multiple Reaction Monitoring Tandem Mass Spectrometry: A Preliminary Study.

Duchenne muscular dystrophy (DMD) is an X-linked recessive disorder characterized by progressive muscle loss, leading to difficulties in movement. Mutations in the DMD gene that code for the protein dystrophin are responsible for the development of DMD disorder, where the synthesis of this protein is completely halted. Therefore, circulating dystrophin protein could be a promising biomarker of DMD disease. Current methods for diagnosing DMD have sensitivity, specificity, and reproducibility limitations. Herein, a quantitative liquid chromatography-tandem spectrometry (LC-MS/MS) technique in multiple reaction monitoring (MRM) mode was designed and validated for accurate dystrophin protein measurement in a dried blood spot (DBS). The method was successfully validated on the basis of international guidelines regarding calibration curves, precision, and accuracy. In addition, patients and healthy controls were used to test the amount of dystrophin protein circulating in DBS samples as a potential biomarker for DMD disorders. DMD patients were found to have considerably lower levels than controls. To the best of our knowledge, this is the first study to report dystrophin levels in DBS through LC-MS/MS as a diagnostic marker for DMD to the proposed MRM method, providing a highly specific and sensitive approach to dystrophin quantification in a DBS that can be applied in DMD screening.

Ultrafast extraction of proteins from tissues using desorption by impulsive vibrational excitation.

A picosecond IR laser (PIRL) can be used to blast proteins out of tissues through desorption by impulsive excitation (DIVE) of intramolecular vibrational states of water molecules in the cell in less than a millisecond. With PIRL-DIVE proteins covering a range of a few kDa up to several MDa are extracted in high quantities compared to conventional approaches. The chemical composition of extracted proteins remains unaltered and even enzymatic activities are maintained.

Diagnosing Parkinson's disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography-mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

NMR-based metabolomics identification of potential serum biomarkers of disease progression in patients with multiple sclerosis.

Multiple sclerosis (MS) is a chronic and progressive neurological disorder, characterized by neuroinflammation and demyelination within the central nervous system (CNS). The etiology and the pathogenesis of MS are still unknown. Till now, no satisfactory treatments, diagnostic and prognostic biomarkers are available for MS. Therefore, we aimed to investigate metabolic alterations in patients with MS compared to controls and across MS subtypes. Metabolic profiles of serum samples from patients with MS (n = 90) and healthy control (n = 30) were determined by Nuclear Magnetic Resonance (1H-NMR) Spectroscopy using cryogenic probe. This approach was also utilized to identify significant differences between the metabolite profiles of the MS groups (primary progressive, secondary progressive, and relapsing-remitting) and the healthy controls. Concentrations of nine serum metabolites (adenosine triphosphate (ATP), tryptophan, formate, succinate, glutathione, inosine, histidine, pantothenate, and nicotinamide adenine dinucleotide (NAD)) were significantly higher in patients with MS compared to control. SPMS serum exhibited increased pantothenate and tryptophan than in PPMS. In addition, lysine, myo-inositol, and glutamate exhibited the highest discriminatory power (0.93, 95% CI 0.869-0.981; 0.92, 95% CI 0.859-0.969; 0.91, 95% CI 0.843-0.968 respectively) between healthy control and MS. Using NMR- based metabolomics, we identified a set of metabolites capable of classifying MS patients and controls. These findings confirmed untargeted metabolomics as a useful approach for the discovery of possible novel biomarkers that could aid in the diagnosis of the disease.

Metabolomics Panel Associated with Cystic Fibrosis-Related Diabetes toward Biomarker Discovery.

The most prevalent comorbidity among cystic fibrosis (CF) patients is cystic fibrosis-related diabetes (CFRD). CFRD has been linked to one of the worse clinical outcomes and a higher mortality. Improved clinical results have been related to earlier diagnosis and treatment of CFRD. Therefore, the present study aimed to investigate the metabolome of human serum of patients with CFRD. This might aid in identifying novel biomarkers linked with the pathophysiology of CFRD and its diagnosis. The liquid chromatography-high-resolution mass spectrometry (LC-HRMS) metabolomics approach was utilized for serum samples from patients with CF (n = 36) and healthy controls (n = 36). Nine patients in the CF group had CFRD, and 27 were non-CFRD patients (nCFRD). A total of 2328 metabolites were significantly altered in CF compared with the healthy control. Among those, 799 significantly dysregulated metabolites were identified between CFRD and nCFRD. Arachidonic acid (AA), ascorbate, and aldarate metabolism were the most common metabolic pathways dysregulated in CF. l-Homocysteic acid (l-HCA) levels were significantly reduced in CF and CFRD compared to the control and nCFRD, respectively. In addition, gamma-glutamylglycine and l-5-hydroxytryptophan (5-HTP) had the highest discrimination between CFRD and nCFRD with AUC (0.716 and 0.683, respectively). These biomarkers might serve as diagnostic biomarkers and aid in understanding potential metabolic changes linked to CF and CFRD.

Exploring the Effects of Vitamin D and Vitamin A Levels on the Response to COVID-19 Vaccine.

COVID-19 vaccines were developed at an unprecedented speed in history. The factors affecting the response to COVID-19 vaccines are not clear. Herein, the effects of vitamin D and vitamin A (retinol) levels on the response to the BNT162b2 vaccine were explored. A total of 124 vaccine recipients were recruited from the general population attending vaccination centers in Irbid, Jordan. Blood samples were collected immediately before receiving the first vaccine dose (D0) and three weeks later (D21). Baseline (D0) levels of 25-hydroxyvitamin D [25(OH)D], retinol, and SARS-CoV-2 S1 IgG antibodies were measured with ELISA. The response to the BNT162b2 vaccine was tested by measuring the levels and avidity of SARS-CoV-2 S1 IgG antibodies on D21. The participants were divided into two groups, unexposed and exposed, based on the D0 SARS-CoV-2 antibody results. No significant correlation was found between the levels of 25(OH)D or retinol and the levels, avidity, or fold increase of antibodies in both groups. Similarly, no significant difference in antibody response was found between 25(OH)D status groups, retinol status groups, or combined status groups. These findings show that the baseline vitamin D or vitamin A levels have no effect on the short-term response to a single dose of BNT162b2 vaccine.

Serum-Based Lipid Panels for Diagnosis of Idiopathic Parkinson's Disease.

Parkinson's disease (PD) is a highly prevalent neurodegenerative movement disorder with an unclear etiology and a lack of definite diagnostic tests and effective treatments. About 95% of PD cases are idiopathic, in which none of the well-known genes underlying familial parkinsonism are mutated. We used untargeted liquid chromatography-mass spectrometry (LC-MS/MS) to profile the serum lipidome of 50 patients with different stages of idiopathic PD (early, mid, or advanced) and 45 age-matched controls. When comparing the PD patients to the control subjects, 169 lipids were significantly altered in both a univariate analysis and a multivariate partial least-squares discriminant analysis (PLS-DA). Compared to the controls, the patients with PD had higher levels of unsaturated triacylglycerides (e.g., TG O-56:9 and TG 52:3), saturated lysophosphatidylcholines (LPC 17:0, 16:0, and 15:0), and hydroxyeicosatetraenoic acid (12-HETE), while lower levels of phosphatidylserines (e.g., PS 40:4 and PS 16:0_22:4), sphingomyelins (SM 42:1), and ceramides (e.g., Cer 40:0 and 42:0) were found between the PD patients and the controls. A panel of 10 significantly altered lipids (PS 40:0, Cer 40:0, Cer 42:0, LPC 17:0, LPC 15:0, PC 37:7, PE O-40:8, PC O-42:4, FA 23:0, and SM 42:1) resulted in a strong receiver operating characteristic curve with an AUC = 0.974. This panel may, therefore, be useful for diagnosing PD. In addition, lipid panels may prove useful for distinguishing among the progression stages of PD. Using one-way ANOVA, 155 lipid species were significantly altered among the PD stages. Parkinson's disease progressed from the early to advanced stages with decreasing levels of PC 31:1, PC 38:4, and LPE 22:5. Conversely, LPC-O 20:0, PC O-42:3, FA 19:0, and FA 22:2 showed an increase in their levels with disease progression. Overall, this study shows an intriguing number of robust changes in specific serum lipids that may become useful for diagnosing PD and its progression, once panels have been validated in larger clinical trials and prospective studies.

Label-free quantitative proteomics analysis for type 2 diabetes mellitus early diagnostic marker discovery using data-independent acquisition mass spectrometry (DIA-MS).

Type-2 diabetes mellitus (T2DM) therapy requires early diagnosis and complication avoidance. Unfortunately, current diagnostic markers do not meet these needs. Data-independent acquisition mass spectrometry (DIA-MS) offers a solution for clinical diagnosis, providing reliable and precise sample quantification. This study utilized DIA-MS to investigate proteomic differential expression in the serum of recently diagnosed T2DM patients. The study conducted a comparative protein expression analysis between healthy and recently diagnosed T2DM groups (discovery cohort). A candidate protein was then validated using enzyme-linked immune assay (ELISA) on serum samples collected from T2DM patients (n = 87) and healthy control (n = 60) (validation cohort). A total of 1074 proteins were identified, and 90 were significantly dysregulated between the two groups, including 32 newly associated with T2DM. Among these proteins, the expression of S100 calcium-binding protein A6 (S100A6) was validated by ELISA. It showed a significant increase in T2DM samples compared to the control group. It was evaluated as a biomarker using the receiver operating characteristic (ROC) curve, consistent with the DIA-MS results. Novel proteins are reported to be involved in the development and progression of T2DM. Further studies are required to investigate the differential expression of candidate marker proteins in a larger population of T2DM patients.

Biomarker discovery in galactosemia: Metabolomics with UPLC/HRMS in dried blood spots.

Introduction:Galactosemia (GAL) is a genetic disorder that results in disturbances in galactose metabolism and can lead to life-threatening complications. However, the underlying pathophysiology of long-term complications in GAL remains poorly understood. Methods: In this study, a metabolomics approach using ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to investigate metabolomic changes in dried blood spots of 15 patients with GAL and 39 healthy individuals. Results: The study found that 2,819 metabolites underwent significant changes in patients with GAL compared to the control group. 480 human endogenous metabolites were identified, of which 209 and 271 were upregulated and downregulated, respectively. PA (8:0/LTE4) and ganglioside GT1c (d18:0/20:0) metabolites showed the most significant difference between GAL and the healthy group, with an area under the curve of 1 and 0.995, respectively. Additionally, the study identified potential biomarkers for GAL, such as 17-alpha-estradiol-3-glucuronide and 16-alpha-hydroxy DHEA 3-sulfatediphosphate. Conclusion: This metabolomics study deepened the understanding of the pathophysiology of GAL and presented potential biomarkers that might serve as prognostic biomarkers to monitor the progression or support the clinical diagnosis of GAL.

Quantitative analysis of soluble costimulatory molecules as potential diagnostic biomarkers for rheumatoid arthritis using LC-MS/MS in MRM mode.

BACKGROUND AND AIMS: Rheumatoid arthritis (RA) is a chronic autoimmune disease. RA-induced immunological responses are coordinated by T-cell stimulation. The costimulatory signal CD28-B7 is essential for T-cell activation by interacting CD28 with CD80 and CD86 costimulatory proteins. CTLA4 is another costimulatory protein that binds to CD80 and CD86 to inhibit T-cell activity. The soluble costimulatory proteins: sCD80, sCD86, sCD28, and sCTLA-4 were detected and quantified in human plasma and correlated with RA development. As potential diagnostic biomarkers for RA, developing a sensitive, specific, and reproducible method for quantifying these costimulatory molecules in human plasma and establishing quantitative ranges for each protein in healthy and RA patients' plasma is essential for advancing the clinical diagnostic and health outcomes. MATERIALS AND METHODS: A novel quantitative liquid chromatography-tandem spectrometry (LC-MS/MS) technique using multiple reaction monitoring (MRM) modes was developed and validated to measure soluble costimulatory molecules sCTLA4, sCD28, sCD80, and sCD86 in human plasma samples. Furthermore, the method was applied to determine sCTLA4, sCD28, sCD80, and sCD86 levels in plasma samples from RA patients (n = 23) and healthy controls (n = 21). RESULTS: The method was successfully developed and validated according to international inter- and intra-assay precision and accuracy guidelines. The linearity of the method was achieved between 0.5 nM and 100 nM for each protein with a correlation coefficient of > 0.998. The plasma level of sCTLA4, sCD80, and sCD86 in RA patients was significantly elevated compared to controls. RA patients had 63.32 ± 17.63 nM sCTLA4 and controls 36.05 ± 18.83 nM; p < 0.0001. The performance of the four proteins was determined using ROC curves, where sCTLA4 showed the highest diagnostic and clinical performance compared to the others. CONCLUSIONS: This study reports the first use of LC-MS/MS in MRM mode to accurately quantify soluble costimulatory molecules in plasma samples as potential RA diagnostic biomarkers. Determination of the reference range for each protein with high selectivity and sensitivity increases the potential for utilizing this method as a clinical diagnostic.

Metabolomics profiling distinctively identified end-stage renal disease patients from chronic kidney disease patients.

Chronic kidney disease (CKD) is a serious public health problem characterized by progressive kidney function loss leading to end-stage renal disease (ESRD) that demands dialysis or kidney transplantation. Early detection can prevent or delay progression to ESRD. The study aimed to gain new insights into the perturbed biochemical reactions and to identify novel distinct biomarkers between ESRD and CKD. Serum samples of 32 patients with ESRD (n = 13) and CKD (n = 19) were analyzed using chemical isotope labeling liquid chromatography-mass spectrometry metabolomics approach. A total of 193 metabolites were significantly altered in ESRD compared to CKD and were mainly involved in aminoacyl-tRNA biosynthesis, branched-chain amino acid (BCAA) biosynthesis, taurine metabolism, and tryptophan metabolism. Three kynurenine derivatives, namely, 2-aminobenzoic acid, xanthurenic acid, and hydroxypicolinic acid were upregulated in ESRD compared to CKD due to the significant decrease in glomerular filtration rate with the progression of CKD to ESRD. N-Hydroxy-isoleucine, 2-aminobenzoic acid, and picolinic acid yielded AUC > 0.99 when analyzed using Receiver Operating Characteristic (ROC) analysis. Our findings suggest that inhibiting the kynurenine pathway might be a promising target to delay CKD progression and that metabolites with high discriminative ability might serve as potential prognostic biomarkers to monitor the progression of CKD to ESRD or used in combination with current markers to indicate the status of kidney damage better.

Investigating the molecular structure of plasma in type 2 diabetes mellitus and diabetic nephropathy by synchrotron Fourier-transform infrared microspectroscopy.

Diabetes mellitus (DM) is associated with a high incidence of morbidity and mortality which, in many cases, is derived from the progressive kidney dysfunction due to diabetic nephropathy (DN). In this study, synchrotron-Fourier-transform infrared (SR-FTIR) microspectroscopy was used to identify molecular changes in the lipid and protein regions in the plasma of patients with different stages of DN (mild, moderate, severe and end-stage), and patients with type 2 diabetes mellitus (T2DM) without DN. Our results revealed different conformational changes in the proteins secondary structure between DN stages, and between DN and T2DM groups illustrated by peak shifts and intensity alterations. End-stage DN showed the highest CH2/CH3 ratio and intensity of the carbonyl group in protein-carbonyl region compared to other DN stages indicating high level of unsaturation and lipid peroxidation and oxidation conditions. Moreover, end-stage DN group was characterized by a decrease in amide I and amide II absorption signals which reflected a sign of hypoalbuminemia. When compared to T2DM, DN group demonstrated a higher oxidation state as confirmed via the high intensity of the carbonyl group and the high level of malondialdehyde. The current study highlights the promising role of SR-FTIR microspectroscopy as a new sensitive analytical approach that can be used to provide better understanding of the pathophysiology of DN, and guide the development of new preventive therapies and treatments.

Effect of natural products use prior to infection with COVID-19 on disease severity and hospitalization: A self-reported cross-sectional survey study.

Background: Managing coronavirus disease 2019 (COVID-19) using available resources is essential to reduce the health burden of disease. The severity of COVID-19 is affected by nutritional status. In this study the effect of natural product use prior to infection with COVID-19 on disease severity and hospitalization was explored. Methods: This was a cross-sectional study. Between March and July 2021, a self-administered survey was conducted in Jordan. Individuals who recovered from COVID-19 and were ≥18 years old were the study population. Study measures included the use of natural products, COVID-19 severity, and hospitalization status. A multivariate regression model was used for statistical analysis. Results: The mean age (mean ± SD) of the study sample (n=2,148) was 40.25 ± 15.58 years old. Multivariate logistic regression showed that the regular intake of carnation (OR [0.56], CI [0.37-0.85]), onion (OR [0.69], CI [0.52-0.92]), lemon (OR [0.68], CI [0.51-0.90]), and citrus fruits (OR [0.66], CI [0.50-0.89]) before infection were associated with a substantial reduction in COVID-19 severity (P<0.01). Also, the consumption of carnation (OR [0.55], CI [0.34-0.88]), lemon (OR [0.57], CI [0.42-0.78]), and citrus fruits (OR [0.61], CI [0.44-0.84]) were associated with a significant decrease in the frequency of COVID-19-induced hospitalization (P<0.01). Conclusions: Regular consumption of carnation, lemon, and citrus fruits before infection was associated with better outcomes for COVID-19. Studies on other populations are required to confirm these findings.

Predictors of COVID-19 severity and hospitalization: A survey-based study from Jordan.

Objectives: To explore the possible predictors of severe illness and hospitalization due to COVID-19 among Jordanians. Method: The study was cross-sectional, survey-based and was conducted from March to July of 2021. Individuals who had recovered from COVID-19 (n = 2148) were recruited in the study. Participants were categorized according to the severity of COVID-19 infection and hospitalization. The study sample was stratified according to age, gender, body mass index (BMI), comorbidities, family income, smoking status, and ABO blood groups. Risk factors were investigated using the Chi-square test and multivariate logistic regression analyses. Results: Severe illness and hospitalization were associated with older age, males, individuals with comorbidities, higher BMI, and lower-income. No significant differences were found in the incidence of severe illness or hospitalization frequency between the ABO groups or between smokers and non-smokers. Multivariate logistic regression analyses predicted male gender, being older than 40, having a BMI of over 30, having 3 or more comorbidities, and low family income as risk factors for severe COVID-19 outcomes. Conclusion: Age was the strongest predictor for severe COVID-19 outcome, followed by having 3 or more comorbidities and to a lesser extent male gender and obesity. These results could help target at-risk groups with infection prevention measures including prioritizing primary COVID-19 vaccines, as well as booster doses.

The impact of vitamin and mineral supplements usage prior to COVID-19 infection on disease severity and hospitalization.

The COVID-19 pandemic has caused a global public health emergency. Nutritional status is suggested to be related to the severity of COVID-19 infection. Herein, we aimed to explore the impact of using vitamin and mineral supplements prior to COVID-19 infection on disease severity and hospitalization. In addition, the prior use of aspirin as an anticoagulant on the disease severity was investigated. A cross-sectional, self-administered survey was conducted between March and July 2021. Recovered COVID-19 individuals (age ≥ 18 years, n = 2148) were recruited in the study. A multivariate logistic regression was used to evaluate the associations of supplements and aspirin use with COVID-19 disease severity and hospitalization status. Among the participants, 12.1% reported symptoms consistent with severe COVID-19, and 10.2% were hospitalized due to COVID-19. After adjustment for confounding variables (age, gender, BMI, cigarette smoking status, and the number of comorbidities), the multivariate logistic regression model showed that the consumption of vitamin D supplements prior to COVID-19 infection was associated with a significant decrease in disease severity (OR = 0.68, 95% CI 0.50 - 0.92; P = 0.01), and a lower risk of hospitalization (OR = 0.64, 95% CI 0.45 - 0.89; P = 0.01). On the other hand, there were no significant differences in the frequencies of severe illness and hospitalizations with the consumption of vitamin A, folic acid, vitamin B12, vitamin B complex, vitamin C, zinc, iron, selenium, calcium, magnesium, omega 3, and aspirin before COVID-19 infection. Among the investigated nutrients, the use of vitamin D prior to COVID-19 infection was associated with reduced disease severity and hospitalization. However, more studies are required to confirm this finding.

Sources of SARS-CoV-2 transmission in Jordan: Self-reported approach.

Background: Understanding the dynamics of virus transmission is essential for controlling the COVID-19 pandemic. Demographic factors could influence transmission of the virus in different communities. Herein, the sources of COVID-19 infection in Jordan were explored. In addition, the effects of demographic factors and the adherence to preventive measures on household transmission were investigated. Methods: The study recruited Jordanian adults who recovered from COVID-19 from March to July 2021. Using a questionnaire, information about participants' demographics, level of adherence to personal protective measures, and their perceived source of COVID-19 infection were collected. Crosstabs were used to test for differences in household transmission ratios between different demographic variables. Logistic regression analysis was used to predict risk factors for household transmission. Results: The study recruited a total of 2313 participants. Household transmission was the most frequently reported source of infection (44.9%). Other sources of transmission were work/education related (16.0%), friends (8.6%), healthcare facilities (4.8%), social/event gathering (3.1%), shopping activities (2.2%), and public transport (1.6%). Significantly higher ratios of household transmission were reported by older adults (>60 years), college/university students, and female participants. No significant difference in household transmission was found between low-income and medium-high income groups. A significant increase in household transmission ratios was found with increased adherence to mask-wearing and social distancing. This could be a reflection of the reduced risk of community transmission with increased adherence to these preventive measures, coupled with the difficulty in adhering to these measures within the household setting. In multivariate logistic regression, females, young adults (18-30 years), older adults (>60 years), and those who adhere to mask-wearing most of the time were associated with an increased risk of infection in the household setting. Conclusion: The results reported in the current study provided an insight into the transmission dynamics of the virus in Jordan, as an example of the MENA region. These findings could be invaluable for the future design of public health policies to control COVID-19 and possibly future pandemics.

CFTR protein quantification as a cystic fibrosis diagnostic biomarker in dried blood spots using multiple reaction monitoring tandem mass spectrometry.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a chloride channel found on the apical surface of epithelial cells in the airway and gastrointestinal tract. A mutation in the CFTR protein is responsible for developing cystic fibrosis (CF) disease. Therefore, circulating CFTR protein could be a promising biomarker of CF disease. Multiple methodological challenges are associated with CF's available diagnostic and screening methods, such as low specificity and potential false discovery rate, mainly for ethnic groups whose CFTR mutations are not covered in the mutation panels. Herein, we have developed an absolute quantification (AQUA) method based on two CFTR signature peptides (SPs). A liquid chromatography-tandem spectrometry (LC-MS/MS) method in multiple reaction monitoring (MRM) mode (MRM transitions 1168.90 > 85.929 and 707.19 > 85.93 of SP1 and SP2, respectively) enabled the accurate quantification of CFTR protein in a dried blood spot (DBS). The method was validated successfully based on international guidelines in terms of signal linearity, precision (within-run CV 3.37-8.54%; between-run CV 5.15-11.06% for the selected SPs), and accuracy (within-run 93.4-105.59%; between-run 97.45-103.28% for the selected SPs). The level of soluble CFTR protein was evaluated as a potential biomarker for CF using patients (n = 39) and healthy controls (n = 30), were found to be in CF patients lower than controls. For instant, the level of signature peptide 1 (SP1) was 2.09 ± 0.55 nM, 68.77 ± 1.40 nM in CF patients compared to Ctrl, respectively; p < 0.0001. This study is the first to report CFTR levels in DBS using signature peptides by LC-MS/MS as a diagnostic marker for CF. The receiver operating characteristic (ROC) for CFTR SP1 and SP2 showed a significant area under the curves (AUC) 0.7714 (99% CI, p < 0.0001), and 0.8234 (99% CI, p < 0.0001), respectively. The presented MRM method provides a highly specific and sensitive approach to CFTR quantification in a DBS and could be applied in CF screening.

Increased Adherence to Infection Control Practices Among Medical Laboratory Technicians During the COVID-19 Pandemic: A Self-Reported Survey Study.

Background: The adherence of medical laboratory technicians (MLT) to infection control guidelines is essential for reducing the risk of exposure to infectious agents. This study explored the adherence of MLT towards infection control practices during the COVID-19 pandemic. Method: The study population consisted of MLT (n = 444) who worked in private and government health sectors in Jordan. A self-reported survey was used to collect data from participants. Findings: More than 87% of the participants reported adherence to hand-washing guidelines and using personal protective equipment (PPE) when interacting with patients (74.5%), and handling clinical samples (70.0%). Besides, 88.1%, 48.2%, and 7.7% reported wearing of lab coats, face masks, and goggles, at all times, respectively. The majority reported increased adherence to infection control practices during the COVID-19 pandemic. This includes increased PPE use at the workplace (94.2%), increased frequency of disinfection of laboratory surfaces (92.4%) and laboratory equipment (86.7%), and increased frequency of handwashing/use of antiseptics (94.6%). Having a graduate degree was significantly associated with increased adherence of participants to the daily use of goggles/eye protection (p = 0.002), and the use of PPE while handling clinical samples (p = 0.011). Having work experience of >10 years was associated with increased adherence to the use of PPE while handling clinical samples (p = 0.001). Conclusion: MLT reported very good adherence with most assessed infection control practices. In addition, they reported increased conformity with infection control guidelines during the COVID-19 pandemic.