BACKGROUND: Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic inflammatory diseases in which innate and adaptive responses of the immune system are induced. RA and PsA have complex signaling pathways. Despite the differences in their clinical presentation, there is a great demand for fast and accurate diagnosis of diseases to implement treatment and plan an individual therapeutic strategy quickly. In this report, we present the results of differential diagnosis of patients with RA and PsA and healthy subjects (C, control group), allowing for reliable differentiation of groups of rheumatoid patients based on biochemical parameters, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectra, and combined data sets.
METHODS: Biochemical analyses, ELISA (enzyme-linked immunosorbent assays), and multiplex assays were conducted for blood sera from patients with RA (n = 32), patients with PsA (n = 28), and the control group (n = 18). ATR-FTIR spectra were collected for lyophilized sera.
RESULTS: The combination of six biochemical parameters (WBC, ESR, RF, CRP, HCC-4/CCL16, and HMGB1/HMGB) allowed the development of the partial least squares discriminant analysis (PLS-DA) model with an overall accuracy (OA) of 80% for test samples. The best separation between RA, PsA, and the control group was obtained utilizing spectral data. Using the interval PLS algorithm (iPLS) specific spectral ranges were selected and a classifier characterized by OA value for test set equal to 88% was obtained. This parameter, for the hybrid PLS-DA model constructed using selected biochemical parameters and a significantly reduced number of spectral variables, reached the level of 84%.
CONCLUSIONS: PLS-DA models developed on the basis of spectral data enable effective differentiation of patients with RA, patients with PsA, and healthy subjects. They appeared to be insensitive to existing inflammation processes which opens interesting perspectives for new diagnostic tests and algorithms for identification of patients with RA and PsA.

The incidence of Candida infections has increased in the last decade, posing a serious threat to public health. Appropriately facing this challenge requires precise epidemiological data on species and antimicrobial resistance incidence, but many countries lack appropriate surveillance programs. This study aims to bridge this gap for Morocco by identifying and phenotyping a year-long collection of clinical isolates (n = 93) from four clinics in Tetouan. We compared the current standard in species identification with molecular methods and assessed susceptibility to fluconazole and anidulafungin. Our results identified limitations in currently used diagnostics approaches, and revealed that C. albicans ranks as the most prevalent species with 60 strains (64.52%), followed by C. glabrata with 14 (15.05%), C. parapsilosis with 6 (6.45%), and C. tropicalis with 4 (4.30%). In addition, we report the first identification of C. metapsilosis in Morocco. Susceptibility results for fluconazole revealed that some isolates were approaching MICs resistance breakpoints in C. albicans (2), and C. glabrata (1). Our study also identified anidulafungin resistant strains in C. albicans (1), C. tropicalis (1), and C. krusei (2), rendering the two strains from the latter species multidrug-resistant due to their innate resistance to fluconazole. These results raise concerns about species identification and antifungal resistance in Morocco and highlight the urgent need for more accurate methods and preventive strategies to combat fungal infections in the country.

BACKGROUND: The integration of artificial intelligence (AI), particularly deep learning models, has transformed the landscape of medical technology, especially in the field of diagnosis using imaging and physiological data. In otolaryngology, AI has shown promise in image classification for middle ear diseases. However, existing models often lack patient-specific data and clinical context, limiting their universal applicability. The emergence of GPT-4 Vision (GPT-4V) has enabled a multimodal diagnostic approach, integrating language processing with image analysis.
OBJECTIVE: In this study, we investigated the effectiveness of GPT-4V in diagnosing middle ear diseases by integrating patient-specific data with otoscopic images of the tympanic membrane.
METHODS: The design of this study was divided into two phases: (1) establishing a model with appropriate prompts and (2) validating the ability of the optimal prompt model to classify images. In total, 305 otoscopic images of 4 middle ear diseases (acute otitis media, middle ear cholesteatoma, chronic otitis media, and otitis media with effusion) were obtained from patients who visited Shinshu University or Jichi Medical University between April 2010 and December 2023. The optimized GPT-4V settings were established using prompts and patients\' data, and the model created with the optimal prompt was used to verify the diagnostic accuracy of GPT-4V on 190 images. To compare the diagnostic accuracy of GPT-4V with that of physicians, 30 clinicians completed a web-based questionnaire consisting of 190 images.
RESULTS: The multimodal AI approach achieved an accuracy of 82.1%, which is superior to that of certified pediatricians at 70.6%, but trailing behind that of otolaryngologists at more than 95%. The model\'s disease-specific accuracy rates were 89.2% for acute otitis media, 76.5% for chronic otitis media, 79.3% for middle ear cholesteatoma, and 85.7% for otitis media with effusion, which highlights the need for disease-specific optimization. Comparisons with physicians revealed promising results, suggesting the potential of GPT-4V to augment clinical decision-making.
CONCLUSIONS: Despite its advantages, challenges such as data privacy and ethical considerations must be addressed. Overall, this study underscores the potential of multimodal AI for enhancing diagnostic accuracy and improving patient care in otolaryngology. Further research is warranted to optimize and validate this approach in diverse clinical settings.

Rat lungworm disease or neuroangiostrongyliasis is a cerebral parasitic infection that affects humans and animals alike. Its clinical signs and symptoms can range from mild self-resolving to serious life-threatening conditions. Studies suggest therapeutic interventions during the early stages of infection to be more effective than in later stages. However, early diagnosis of infection is usually problematic without the knowledge of exposure and/or detection of the parasite\'s DNA or antibody against the parasite in the cerebrospinal fluid. This requires a lumbar puncture, which is an invasive procedure that generally requires hospitalization. This study evaluates an affordable and less invasive alternative to detect parasitic DNA by PCR from the peripheral blood of potentially infected animals. Blood samples from 58 animals (55 dogs and 3 cats) with clinical suspicion of infection were submitted to our lab between February 2019 and August 2022 by local, licensed veterinarians. DNA was extracted from whole blood, plasma, serum, and/or packed cells using the Qiagen DNeasy Blood & Tissue Kit as per the manufacturer\'s protocol. All 58 animals were tested by real-time PCR using the AcanITS1 assay and 32 of these animals (31dogs; 1 cat) were also tested using the AcanR3990 assay. The PCR results for both assays were classified into strongly positive > positive > weakly positive > negative, and equivocal for ambiguous results, based on the strength of the signal. The percent infection detected using the AcanITS1 and AcanR3990 assays was 12.72% (7/55) and 20.68% (6/29), respectively. The overall percent infection detected was 34.37% (11/32), with only two animals testing positive by both assays. The three cats involved in this study tested negative by both assays. These results are promising and warrant further investigations to increase sensitivity including variables that might affect detection in the blood, such as parasite load, and laboratory methodologies.

Integrating isothermal nucleic acid amplification strategies into immunoassays can significantly decrease analytical limits of detection (LODs). On the other hand, an amplification step adds time, complication, reagents, and costs to the assay format. To evaluate the pros and cons in the context of heterogeneous multistep immunoassays, we quantified prostate-specific antigen (PSA) with and without rolling circle amplification (RCA). In addition, we compared time-gated (TG) with continuous-wave (CW) photoluminescence (PL) detection using a terbium complex and a fluorescein dye, respectively. For both direct (non-amplified) and amplified assays, TG PL detection provided circa four- to eightfold lower LODs, illustrating the importance of autofluorescence background suppression even for multi-wash assay formats. Amplified assays required an approximately 2.4 h longer assay time but led to almost 100-fold lower LODs down to 1.3 pg/mL of PSA. Implementation of TG-FRET (using a Tb-Cy5.5 donor-acceptor pair) into the RCA immunoassay resulted in a slightly higher LOD (3.0 pg/mL), but the ratiometric detection format provided important benefits, such as higher reproducibility, lower standard deviations, and multiplexing capability. Overall, our direct comparison demonstrated the importance of biological background suppression even in heterogeneous assays and the potential of using isothermal RCA for strongly decreasing analytical LODs, making such assays viable alternatives to conventional enzyme-linked immunosorbent assays (ELISAs).

OBJECTIVE: The aim of this study on native human cadavers was to compare clinical, sonographic, and radiological measurements of fenestrations, dehiscences, and 3-wall bone defects on implants.
METHODS: The examination was carried out on five human mandibles. After the insertion of 27 implants, dehiscences (n = 14), fenestrations (n = 7) and 3-wall bone defects (n = 6) were prepared in a standardized manner. The direct measurement of the bone defects was carried out with a periodontal probe and the radiological examination was carried out using digital volume tomography (DVT). The ultrasound examination (US) was performed using a clinical 24-MHz US imaging probe. Means and standard deviations of the direct, US, and DVT measurements were calculated. Measurements were statistically compared using the Pearson correlation coefficient and Bland-Altman analysis.
RESULTS: Bone defects were on average 3.22 ± 1.58 mm per direct measurement, 2.90 ± 1.47 mm using US, and 2.99 ± 1.52 mm per DVT assessment. Pairwise correlations of these measurements were R = .94 (p < .0001) between direct and US, R = .95 (p < .0001) between DVT and US, and R = .96 (p < .0001) between direct and DVT. The mean differences of the measurements (and 95% CI) between direct and US was 0.41 (-0.47 to 1.29), US and DVT 0.33 (-0.30 to 0.97), and direct and DVT 0.28 (-0.50 to 1.07).
CONCLUSIONS: All peri-implant bone defects could be identified and sonographically measured. US measurements showed a strong correlation with direct and DVT measurements. The sonographic measurement accuracy was highest for dehiscences, followed by fenestrations and 3-wall bone defects.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) of circulating cell-free DNA from plasma is a hypothesis-independent broadband diagnostic method for identification of potential pathogens. So far, it has only been investigated in special risk populations (e.g. patients with neutropenic fever).
OBJECTIVE: To investigate the extent to which mNGS (DISQVER® platform) can be used in routine clinical practice.
METHODS: We collected whole blood specimens for mNGS testing, blood cultures (BC), and pathogen-specific PCR diagnostics. Clinical data and pathogen diagnostics were retrospectively reviewed by an infectious disease expert panel regarding the adjustment of anti-infective therapy.
RESULTS: In 55 selected patients (median age 53 years, 67% male) with heterogeneous diagnoses, a total of 66 different microorganisms and viruses were detected using mNGS (51% viruses, 38% bacteria, 8% fungi, 3% parasites). The overall positivity rate of mNGS was 53% (29/55). Fifty-two out of 66 (79%) potential pathogens detected by mNGS were found in patients with primary or secondary immunodeficiency. The concordance rates of BC and pathogen-specific PCR diagnostics with mNGS testing were 14% (4/28) and 36% (10/28), respectively (p < 0.001). An additional bacterial pathogen (Streptococcus agalactiae) could only be detected by BC. Therapeutic consequences regarding anti-infective therapy were drawn from 23 pathogens (35% of detections), with 18 of these detections occurring in patients with immunodeficiency.
CONCLUSIONS: We conclude that mNGS is a useful diagnostic tool, but should only be performed selectively in addition to routine diagnostics of infectious diseases. The limited number of patients and the retrospective study design do not allow any further conclusions.

BACKGROUND: Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited.
OBJECTIVE: This study aimed to assess the longitudinal changes in the accuracy of differential diagnosis lists created by an AI-based symptom checker used in the real world.
METHODS: This was a single-center, retrospective, observational study. Patients who visited an outpatient clinic without an appointment between May 1, 2019, and April 30, 2022, and who were admitted to a community hospital in Japan within 30 days of their index visit were considered eligible. We only included patients who underwent an AI-based symptom checkup at the index visit, and the diagnosis was finally confirmed during follow-up. Final diagnoses were categorized as common or uncommon, and all cases were categorized as typical or atypical. The primary outcome measure was the accuracy of the differential diagnosis list created by the AI-based symptom checker, defined as the final diagnosis in a list of 10 differential diagnoses created by the symptom checker. To assess the change in the symptom checker\'s diagnostic accuracy over 3 years, we used a chi-square test to compare the primary outcome over 3 periods: from May 1, 2019, to April 30, 2020 (first year); from May 1, 2020, to April 30, 2021 (second year); and from May 1, 2021, to April 30, 2022 (third year).
RESULTS: A total of 381 patients were included. Common diseases comprised 257 (67.5%) cases, and typical presentations were observed in 298 (78.2%) cases. Overall, the accuracy of the differential diagnosis list created by the AI-based symptom checker was 172 (45.1%), which did not differ across the 3 years (first year: 97/219, 44.3%; second year: 32/72, 44.4%; and third year: 43/90, 47.7%; P=.85). The accuracy of the differential diagnosis list created by the symptom checker was low in those with uncommon diseases (30/124, 24.2%) and atypical presentations (12/83, 14.5%). In the multivariate logistic regression model, common disease (P<.001; odds ratio 4.13, 95% CI 2.50-6.98) and typical presentation (P<.001; odds ratio 6.92, 95% CI 3.62-14.2) were significantly associated with the accuracy of the differential diagnosis list created by the symptom checker.
CONCLUSIONS: A 3-year longitudinal survey of the diagnostic accuracy of differential diagnosis lists developed by an AI-based symptom checker, which has been implemented in real-world clinical practice settings, showed no improvement over time. Uncommon diseases and atypical presentations were independently associated with a lower diagnostic accuracy. In the future, symptom checkers should be trained to recognize uncommon conditions.

BACKGROUND: People with HIV (PHIV) admitted to hospital have high mortality, with tuberculosis (TB) being the major cause of death. Systematic use of new TB diagnostics could improve TB diagnosis and might improve outcomes.
METHODS: We conducted a cluster randomised trial among adult PHIV admitted to Zomba Central Hospital, Malawi. Admission-days were randomly assigned to: enhanced TB diagnostics using urine lipoarabinomannan (LAM) antigen tests (SILVAMP-LAM, Fujifilm, Japan and Determine-LAM, Alere/Abbot, USA), digital chest X-ray with computer aided diagnosis (dCXR-CAD, CAD4TBv6, Delft, Netherlands), plus usual care (\"enhanced TB diagnostics\"); or usual care alone (\"usual care\"). The primary outcome was TB treatment initiation during admission. Secondary outcomes were 56-day mortality, TB diagnosis within 24-hours, and undiagnosed TB at discharge, ascertained by culture of one admission sputum sample.
RESULTS: Between 2 September 2020 and 15 February 2022, we recruited 419 people. Four people were excluded post-recruitment, leaving 415 adults recruited during 207 randomly assigned admission-days in modified intention-to-treat analysis. At admission, 90.8% (377/415) were taking antiretroviral therapy (ART) with median (IQR) CD4 cell count 240 cells/mm3. In the enhanced diagnostic arm, median CAD4TBv6 score was 60 (IQR: 51-71), 4.4% (9/207) had SILVAMP-LAM-positive and 14.4% (29/201) had Determine-LAM positive urine with three samples positive by both urine tests. TB treatment was initiated in 46/208 (22%) in enhanced TB diagnostics arm and 24/207 (12%) in usual care arm (risk ratio [RR] 1.92, 95% CI 1.20-3.08). There was no difference in mortality by 56 days (enhanced TB diagnosis: 54/208, 26%; usual care: 52/207, 25%; hazard ratio 1.05, 95% CI 0.72-1.53); TB treatment initiation within 24 hours (enhanced TB diagnosis: 8/207, 3.9%; usual care: 5/208, 2.4%; RR 1.61, 95% CI 0.53-4.71); or undiagnosed microbiological-confirmed TB at discharge (enhanced TB diagnosis, 0/207 (0.0%), usual care arm 2/208 (1.0%) (p = 0.50).
CONCLUSIONS: Urine SILVAMP-LAM/Determine-LAM plus dCXR-CAD diagnostics identified more hospitalised PHIV with TB than usual care. The increase in TB treatment appeared mainly due to greater use of Determine-LAM, rather than SILVAMP-LAM or dCXR-CAD. Poor concordance between Determine-LAM and SILVAMP-LAM urine tests requires further investigation. Inpatient mortality for adults with HIV remains unacceptability high.

COVID-19 has been notoriously unpredictable in its clinical course. Such unpredictability poses a challenge to clinicians in predicting patients who will develop severe cases and possibly die from the infection. This study aims to assess and compare the diagnostic value of the NLR and SII as biomarkers in predicting COVID-19 severity, represented by mortality, with a multicentre comparative study including 855 patients in Saudi Arabia. Descriptive and analytical statistics were used to compare haematological indices between survivors and non-survivors. The median age of patients included was 41 years old, with an almost equal ratio of men to women. Most participants were Saudis, and the mortality rate in the study cohort was 13.22%. Non-survivors, as compared to survivors, were significantly older, had lower RBC counts, haemoglobin and haematocrit levels, as well as significantly higher WBC and neutrophil counts. Both the NLR and SII were capable of differentiating between survivors and non-survivors, with the latter having significantly higher values. However, the NLR was superior to the SII in such differentiation, as it had a larger area under the curve. This study further confirms the diagnostic values of the NLR and SII as biomarkers in predicting COVID-19 severity and mortality, with the NLR being more sensitive and specific. Clinical guidelines on managing COVID-19 cases should benefit from these findings by harnessing the value of the NLR in COVID-19 management.