Sousa

We used a paper and pen questionnaire. The questionnaire included demographic data and formal (not dialect) Arabic translation of the whole PHQ. PHQ consists of six modules. Depression (PHQ9-9 items), generalized anxiety (GAD7-7 items), and somatization (PHQ15-15 items) modules have items with Likert scales. Panic (15 items), eating (8 items), and alcohol abuse (5 items) modules are all Yes/No answers. The Arabic version is exactly the same structure of the original English scale. We followed the guidelines of Sousa et al. in translation, adaptation, and validation of PHQ [23 (link)]: Step 1: forward translation—translation of the PHQ into the Arabic language by two independent translators. Step 2: synthesis I—comparison of the two translated versions of the PHQ and the development of an initial translated version. Step 3: blind back-translation of the preliminary initial translated version of the PHQ from Arabic to English. Step 4: synthesis II—comparison of the two back-translated versions of the PHQ. Step 5: pilot testing of the pre-final version of the instrument in Arabic. We also did face validity by sending the pre-final version to eight referees from mental health experts.

Place, duration, and design of the study
This prospective single-center study was performed in our department between May 2021 and December 2021.
Ethics
Informed consent was obtained for all patients. The examinations were only performed after a careful explanation of the characteristics, non-invasiveness, and aim of the study. The study was approved by the Ethics Committee of Centro Hospitalar Universitário do Porto (Number: 2021.93 [075-DEFI/078-CE]) and the design complies with the Declaration of Helsinki ethical standards.
Inclusion criteria
Adulthood, OD concomitant with SARS-CoV-2 documented infection), subjective persistence of OD, and a cognitive status that allowed the patient to sign an informed consent and to self-treat with the medical therapeutic proposed.
Exclusion criteria
Chronic rhinosinusitis, recent head trauma with loss of consciousness, olfactory complaints before documented COVID-19, gestation, prior nasal surgery, known olfactory bulb lesion on imaging, neurologic or psychiatric disease, or inability to tolerate nasal endoscopy.
Evaluation
Our evaluation consisted of several steps: A general assessment of days before the onset of hyposmia, co-morbidities, a subjective assessment using the Portuguese Language Olfactory Disorders Questionnaire [12 (link)], and a VAS toward subjective impairment of hyposmia in quality of life. Our VAS consisted of an 11-point scale ranging between 0 and 10, being “not a problem” on the left end of the scale (number 0) and “worst problem in my life” on the right end of the scale (number 10). An objective assessment of olfactory thresholds using the Sniffin´ Sticks threshold test with n-butanol: 16 levels in 48 pens were also performed [13 (link)]. The nasal status assessment was performed by nasal endoscopy for exclusion of nasal pathology and evaluation of Lund-Kennedy score - when a polyp score ≥ 1 was seen, the patient was excluded from our cohort while follow-up and further management were maintained in parallel. Also, all patients underwent olfactory training and adjuvant therapy using the strategy described in the protocol described by Sousa et al. [14 (link)].
Variables evaluated
Age, gender, relevant comorbidities, date of perceived onset of OD, olfactory thresholds, and VAS (related to OD). Patients were re-evaluated after three months, and data was collected.
Statistical analysis
Collected data were analyzed using SPSS version 26 (Statistical Package for Social Studies) - IBM, USA. For numerical values, the range, mean, and standard deviations were calculated. The differences between the two mean values were used using the Mann-Whitney U test. Differences in mean values before and after the intervention were done by Wilcoxon signed ranks test. The correlation between VAS and olfactory thresholds was done using Pearson’s correlation coefficient. To access the confounding variables, ANCOVA analysis was also performed. All reported p-values are two-tailed, with a p-value ≤ 0.05 indicating statistical significance.

The label‐free protein quantifications (precursor areas) for the colorectal tumours (Zhang et al, 2014 (link)) and the tandem mass tag (TMT) protein intensities for the breast and colorectal cancer cell lines (Lawrence et al, 2015 (link); Lapek et al, 2017 (link); Roumeliotis et al, 2017 (link)) were pre‐processed and transformed to log2 fold‐changes as described previously (Sousa et al, 2019 (link)). For the brain (Petralia et al, 2020 (link)), lung (Gillette et al, 2020 (link)) and stomach (Mun et al, 2019 (link)) cancers, the sample replicates were combined by averaging the log2 fold‐change values of each protein. After that, we removed six outlier samples from colorectal cancer with an absolute median log2 fold‐change distribution higher than 1 (2‐fold). Altogether, we assembled a matrix with 14,742 proteins and 1,266 samples (1,170 cancer samples and 96 cell lines) belonging to nine different tissues. This matrix contained 9,941,918 protein measures (8,721,454 missing values) and 5,052 proteins quantified in at least 80% of the samples.

To explore the influence of sea lamprey parasitism on siscowet lake trout reproduction and growth, we first developed a base DEB model that described the energy allocation and dynamics of siscowet lake trout throughout their entire lifecycle that accounts for energetic tradeoffs constrained by life history. The general structure, equations and assumptions of DEB models have been thoroughly covered previously (Sousa et al., 2008 (link), 2010 (link); Kooijman, 2010 (link); Jusup et al., 2017 (link)). Briefly, DEB models are described by four state variables (reserve energy, structural mass, cumulative energy invested to maturation for juveniles and energy invested in reproduction for adults), and a set of differential equations and model parameters dictate energy flux to each compartment (Kooijman, 2010 (link)) (Figure 1). Energy enters an organism through uptake of food (with a fraction removed as feces) and enters a reserve pool. In DEB models, reserve represents all tissue that does not require energy for maintenance and is readily metabolizable as a source of usable energy (Jusup et al., 2017 (link)). Energy is then mobilized from the reserve at a given rate and allocated towards somatic functions and maturation/reproduction following the κ-rule. The κ-rule states that a fixed portion (κ) of mobilized energy is allocated towards somatic maintenance (e.g. maintenance of existing structural mass, mean level of movement costs and production of scales) and growth (increase in structural mass), while the remaining fraction (1-κ) is allocated towards maturity maintenance and maturation (for juveniles) or reproduction (for adults). Maturation involves continuous energy investment as the organism becomes more complex and prepares the body for the mature adult state. For example, the preparation of reproductive machinery and development of immune defense systems require more energy as an organism matures (Kooijman, 2010 (link)). Maturity maintenance is the energy spent to maintain the current state of complexity. DEB models handle maturity by tracking the cumulative investment of energy towards maturation, and once a specified threshold is reached (called puberty), mobilized energy is then allocated towards a reproductive buffer for later allocation to reproductive activities, such as egg production (Kooijman, 2010 (link); Jusup et al., 2017 (link)). Somatic and maturity maintenance processes (e.g. protein turnover, activity, immune function, metabolizing and excreting toxicants, etc.) have priority and are paid first before remaining energy can be allocated to growth or the reproduction buffer. A portion of the energy allocated to reproduction matures to ripe reproductive matter (hereafter referred to as ovarian mass; Kooijman, 2010 (link)). Table 1 summarizes the state variables and their dynamics, and a generalized overview of energy allocation processes is shown in Figure 1.
The standard (std) DEB model is the simplest model in the family of DEB models which can be adapted to model most species (Marques et al., 2018 (link)). Because DEB models are adaptable to any species, they use terminology that attempts to be species generic. We used the abj typified DEB model that accounts for metabolic acceleration, a DEB term that refers to rapid growth during early development, following initiation of exogenous feeding (generalized as birth in DEB terminology) (Kooijman, 2014 (link); Lika et al., 2014 (link)). Metabolic acceleration occurs well before the maturity threshold for puberty and might or might not coincide with metamorphosis (a DEB term that refers to rapid change in morphology). Although lake trout do not undergo metamorphosis, they do undergo rapid growth post-hatch making the abj model appropriate. The abj DEB model differs from the std DEB model by allowing for the rapid increase in respiration and change in body shape that occurs during the larval or post-hatch stages of most fish species and includes one additional parameter, the maturity threshold at metamorphosis The abj model has been used for many actinopterygians (Lika et al., 2022 (link)).
Because we are exploring the effects of parasitism, a substantial stressor that potentially affects maintenance costs, we also implemented rules that describe energy use when available energy in the κ fraction is not sufficient to meet somatic maintenance demands (see Table 1). If there is insufficient energy available to meet somatic maintenance requirements, growth ceases and maintenance costs are paid from the energy available for reproductive functions (i.e. reproductive buffer and/or ovarian mass in proportion to their availability). If there is insufficient energy available in the κ fraction, ovarian mass and the reproductive buffer, energy is then taken from structure and the organism loses structural mass or “shrinks” (Augustine et al., 2014 (link)).