Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 × 10${}^9$ cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 × 10${}^9$ cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer.

Objective: Investigate the effect of bilateral ultrasound-guided quadratus lumborum block (QLB) in the need for additional opioid administration in response to intraoperative nociception during ovariectomy in cats. Methods: Thirty-six feral cats were randomly allocated to group control (C) or group QLB with 0.4 mL kg⁻¹ of 0.25% bupivacaine per hemiabdomen. Hemodynamic data were collected at five intraoperative time points. A 20% increase in pulse rate or respiratory rate was treated with fentanyl. Results: Pulse rate and respiratory rate were higher in the C group compared to the QLB group during ovarian manipulation. No differences in systolic and mean arterial pressure and hypotension were comparable in both groups. The QLB group required less administration of intraoperative fentanyl than the C group at 4/16 and 10/16, respectively (p < 0.05). Conclusions: The bilateral QLB with bupivacaine led to a reduction in opioid consumption in feral cats undergoing elective ovariectomy.

Background and Aim: Acute viral gastroenteritis is one of the main causes of hospitalization in dogs during the 1st year of life. This retrospective study aimed to describe a pediatric canine population presumptively diagnosed with acute viral gastroenteritis and to identify potential prognostic factors that influence hospitalization time (HT) and mortality. Materials and Methods: Canine patients up to 12 months of age diagnosed with presumptive acute viral gastroenteritis were searched retrospectively from two veterinary hospitals during a 5-year period (2015–2020). Information regarding patient signalment, prophylactic care, clinical signs, blood test results, presence of systemic inflammatory response syndrome, and additional treatments were recorded to analyze their association with HT and mortality. Only dogs with a complete medical record until death or discharge were included in the study. Results: Ninety-four dogs were identified: 76 dogs (80.9%) survived with a median HT of 5 days (range: 2–16 days) and 18 dogs (19.1%) died with a median HT of 3½ days (range: 1–8 days) after admission. The presence of fever and fresh frozen plasma (FFP) administration was significantly associated with a lower survival rate (p = 0.021 and p = 0.037) in the multivariate analysis. Among survivors, incomplete primo-vaccination, the presence of hematochezia, and FFP administration were considered independent predictors of time to clinical recovery (p = 0.026, p = 0.047, and p = 0.026, respectively), being associated with higher HT. Conclusion: The presence of fever and FFP administration was significantly associated with a lower survival rate. An inadequate primo-vaccination status prior to admission, hematochezia, and FFP administration was associated with longer HT in surviving patients. Further studies are needed to confirm the present results.

Total white blood cells (WBC) count is an important indication for infection diagnosis, in both human and veterinary medicine. State-of-the-art WBC counts are performed by flow cytometry combined with light scattering or impedance measurements, in the clinical analysis laboratory. These technologies are complex and difficult to be miniaturized into a portable point-of-care (POC) system. Spectroscopy is one of the most powerful technologies for POC miniaturization due to its capacity to analyze low sample quantities, little to no sample preparation, and ‘real-time’ results. WBC is in the proportion of 1:1000 to red blood cells (RBC), and the latter dominate visible-near infrared (Vis-NIR) information due to their large quantities and hemoglobin absorbance. WBC are difficult to be detected by traditional spectral analysis because their information is contained within the interference of hemoglobin bands. Herein, we perform a feasibility study for the direct detection of WBC counts in canine blood by Vis-NIR spectroscopy for veterinary applications, benchmarking current chemometrics techniques with self-learning artificial intelligence—a new advanced method for high-accuracy quantification from spectral information. Results show that total WBC counts can be detected by Vis-NIR spectroscopy to an average detection limit of 7.8 $\times {10}^9$ cells/L, with an R${}^2$ of 0.9880 between impedance flow cytometry analysis and spectral quantification. This result opens new possibilities for reagent-less POC technology in infection diagnosis. As WBC counts in dogs range from 5 to 45 $\times {10}^9$ cells/L, the detection limit obtained in this research allows concluding that the combined use of spectroscopy with this SL-AI new algorithm is a step towards the existence of portable and miniaturized Spectral POC hemogram analysis.

Thrombocytosis is a disorder with an excessive number of platelets in the blood, where total platelet counts (TPC) are crucial for diagnosis. This condition predisposes to blood vessels clotting and diseases such as stroke or heart attack. TPC is generally performed at the laboratory by flow cytometry with laser scattering or impedance detection. Due to the limited capacity of automated hematology in performing TPC quantification, a manual microscopy count is a very common quality assurance measure undertaken by clinical pathologists. Monitoring coagulation risk is key in many health conditions, and point-of-care platforms would simplify this procedure by taking platelet counts to the bedside. Spectroscopy has high potential for reagent-less point-of-care miniaturized technologies. However, platelets are difficult to detect in blood by standard spectroscopy analysis, due to their small size, low number when compared to red blood cells, and low spectral contrast to hemoglobin. In this exploratory research, we show that it is possible to perform TPC by advanced spectroscopy analysis, using a new processing methodology based on self-learning artificial intelligence. The results show that TPC can be measured by visible–near-infrared spectroscopy above the standard error limit of 61.19 × 10${}^9$ cells/L (R${}^2$ = 0.7016), tested within the data range of 53 × 10${}^9$ to 860 × 10${}^9$ cells/L of dog blood. These results open the possibility for using spectroscopy as a diagnostic technology for the detection of high levels of platelets directly in whole blood, towards the rapid diagnosis of thrombocytosis and stroke prevention.