Layered magneticmaterials are becoming a major platform for futurespin-based applications. Particularly, the air-stable van der Waalscompound CrSBr is attracting considerable interest due to its prominentmagneto-transport and magneto-optical properties. In this work, weobserve a transition from antiferromagnetic to ferromagnetic behaviorin CrSBr crystals exposed to high-energy, non-magnetic ions. Alreadyat moderate fluences, ion irradiation induces a remanent magnetizationwith hysteresis adapting to the easy-axis anisotropy of the pristinemagnetic order up to a critical temperature of 110 K. Structure analysisof the irradiated crystals in conjunction with density functionaltheory calculations suggests that the displacement of constituentatoms due to collisions with ions and the formation of interstitialsfavors ferromagnetic order between the layers. Up to the Néeltemperature of TN = 132 ± 1 K, theFM spin order within each layer is compensated by an AFM arrangementof the vdW layers in the out-of-plane direction (c-axis),15 which is the reason for thevanishing net magnetization of pristine bulk crystals. First-principlescalculations of magnetic moments localized on the Cr ions predicta magnetic order up to 160 K in a single layer.16 Secondharmonic generation17 and magneto-transportmeasurements18 determined the FM order of asingle CrSBr layer to be around 150 K. Overall, bulk CrSBr is characterizedby a relatively weak AFM coupling between the vdW layers, which isalso apparent from the fact that a small field of 0.4 T applied alongthe easy-axis (b-axis) is sufficient to switch themagnetic order from AFM to FM.11 The weakinteraction between the layers makes CrSBr particularly susceptibleto the modification of the magnetic order by external stimuli. Besidesthe recently demonstrated feasibility of strain and ligand substitutionin altering the magnetic properties of CrSBr,22 ion irradiation may thus be aviable tool for modifying the magnetic structure with additional benefits,such as local patterning.Due to the pronounced correlationsbetween magnons, photons, electrons,and phonons,Here, we report a change of the magneticinterlayer coupling inbulk and few-layer CrSBr crystals irradiated with nonmagnetic ions.In the absence of an applied field and below a critical temperatureof 110 K, the irradiated samples show sizable spontaneous magnetizationwhich adapts to the easy-axis anisotropy of the pristine magneticorder and marks the transition of the magnetic ground state of irradiatedCrSBr crystals from AFM to FM. Raman spectroscopy evidences the ion-fluence-dependentsoftening of the crystal lattice that is indicative of the formationof a large number of crystallographic defects. In conjunction withstructure analysis, first-principles calculations suggest that thedisplacement of the atoms, particularly Cr, into interstitial positionsbetween the vdW layers favors FM interlayer coupling. Our study highlightsthe potential of ion irradiation for nonchemical engineering of magnetismin vdW crystals.Supporting Information, Methods). Micro-Raman spectroscopy is performed using a linearlypolarized, continuous-wave, 532 nm Nd:YAG laser for excitation, andmagnetic properties are measured by a superconducting quantum interferencedevice  magnetometer.Millimeter-sized bulk CrSBr crystals are synthesizedusing a chemicalvapor transport method .To complement our investigation of this crystal, we study a secondtype of sample containing many small CrSBr flakes with a thicknessof less than 1 μm that we obtain by exfoliation onto a siliconsubstrate. To achieve irradiation effects comparable to those in thelarge bulk crystal, we vary the energy of the He+ ionsfor the thin flake sample .In our study,we subjected two types of CrSBr samples to Heg modes of the out-of-plane vibration,can be clearly identified in the Raman spectra (cf. 23 The Ag1 (113.8 cm–1) and Ag3 (342.5 cm–1) modesshow maximum intensity when the laser polarization is parallel tothe b-axis, while the Ag2 (245.2 cm–1) modeis most pronounced when the polarization is aligned with the a-axis, reflecting the strong structural anisotropy of thecrystal structure. Even CrSBr crystals exposed to the largest ionfluences applied in our study maintain their atomic structure andthis anisotropy. The gradual decrease in the peak intensity of theRaman signatures and the continuous shift toward smaller wavenumbersobserved in Figure S1) are generallyattributed to a softening of the phonon modes resulting from defect-inducedvariations in the lattice spacing.24 Thisobservation is in line with a recent study of defects in mono- andfew-layer CrSBr flakes irradiated by He ions, which also demonstratesthe structural and electronic anisotropy.25As depicted in ctra cf. 1b,c,d.2326 we calculate the average number of times anatom is displaced from its equilibrium lattice position during irradiation, which allows us to estimate the depthprofile of energy transferred from the energetic ions to the crystalmatrix and thus the distribution of defects created in the crystal.The DPA of the 200-μm-thick crystal shown in From SRIM (Stopping and Range of Ions in Matter) simulations,+ irradiationon themagnetic properties, we measure the field- and temperature-dependentmagnetization in the 200-μm-thick bulk crystal and in the thin-flakesample using SQUID-magnetometry. Figure S2 show the magnetization induced in the bulk crystal by a field appliedalong the b-axis. Before irradiation, the sampleexhibits the typical AFM response expected for pristine CrSBr crystals.7 However, after we expose the crystal to ion irradiation,magnetic hysteresis centered at zero field is observed at fields upto 0.1 T. Upon increasing the irradiation fluence, this fingerprintof FM order becomes more and more prominent. At the same time, atfields around 0.5 T, we also observe a spin-flip transition that ischaracteristic of AFM-ordered CrSBr . This observation is fully in line with the fact that onlya fraction of the volume of the 200-μm-thick crystal is alteredby ion irradiation. Hence, we observe a signal that has contributionsfrom both the irradiated and pristine regions of the sample. For afluence of 8 × 1015/cm2, we can estimatethat the saturation magnetic moment in the irradiated volume amountsto 2 μB/Cr by assuming an irradiation thickness of6 μm according to Figure S3).To investigate the influence of HeFigure S2, magnetic hysteresis centered at zerofield dominates the magnetic response after irradiation and the signaturesof the AFM spin-flip transition are no longer observed. The saturationmagnetization approaches 2 μB/Cr. Note that the saturationmagnetization of Cr atoms in CrSBr is 3 μB/Cr.28 For the calculation, we assumed that the saturation magnetic momentis 3 μB/Cr in the pristine sample, and there is noflake loss during sample handling. The in-plane magnetic anisotropyis not observed as a sharp spin-flip transition because individualflakes are randomly oriented on this sample.The effect of ionirradiation on the magnetic properties is muchmore prominent in the second sample. For the thin flakes, we observea full transition of the magnetic ground state from the AFM to FMafter ion irradiation. As demonstrated by + ions, however, weobserve a continuous increase of the magnetization of thin flakesas the temperature decreases . Klein et al. suggested a potentialdefect-related phase after annealing with an onset arising also ataround 110 K.29To characterize the remanence of the irradiation-inducedFM state,CrSBr samples are cooled from room temperature, while a field of0.1 T is applied to saturate the FM magnetization. When the temperaturereaches 2 K, the magnetic field is set to zero. We then measure theremanence while increasing the temperature. The irradiated 200-μm-thickcrystal exhibits sizable spontaneous magnetization below the criticaltemperature, as can be clearly seen in b-axis, while the a- and c-axis correspond to intermediate and hardmagnetic axes. In another ion-irradiated bulk crystal sample, we measurethe temperature-dependent remanence for fields applied along the a-, b-, and c-axis duringcool-down. As Figure S5 shows, the amplitudeof the remanence is largest when the field is applied along the b-axis. Ion irradiation has changed the magnetic groundstate, but the magnetic easy axis is preserved. This result is ingood agreement with the Raman measurements presented above, indicatingthat the crystalline anisotropy of CrSBr remains present even afterirradiation, with the largest ion fluences used in our experiments.As outlined above,pristine CrSBr shows a magnetic easy axis alongthe crystallographic 31The transfer of energy from high-energy ions to atoms in the crystallattice leads to the formation of defects with very high concentrations.In our case here, the SRIM simulation results in a defect concentrationof around 1% for the flake sample and the top 4 μm of the bulksample 2a,b.The29 which is consistent with our lowest vacancy formation energyfound for Br (3.59 eV/atom). Table S2 liststhe total magnetic moments for various vacancies and interstitials.While the net magnetic moment in pristine CrSBr is zero, our calculationsshow total magnetic moments of 2.0, 6.04, and 1.05 μB/supercell for S, Cr, and Br vacancies, respectively. In contrast,the S interstitial does not significantly change the magnetic momentsof the pristine bilayer. The presence of magnetic character can berelated to the shift between the spin-up- and spin-down-associatedstates in the electronic structure of the defective materials, aspresented in Figures S6 and S7.We calculated the energeticsof different types of vacancies andinterstitials using 4 × 4 CrSBr bilayer supercells correspondingto a defect concentration of ∼0.5%. The interstitial atomswere initially placed at different positions, and the most energeticallyfavorable structure was identified. The formation energies for Cr,S, and Br vacancies and interstitials are summarized in Table S3). The effect is more pronounced in the CrSBr bilayer with Cr interstitials.The optimized structure of the CrSBr bilayer with Cr interstitial indicates that the interstitialatom chooses a position in the void between the van der Waals gapatoms forming a covalent bond between the layers, which is consistentwith the previous results.32 The first-nearest-neighborexchange interactions between Cr–Br–Cr and Cr–S–Cr,as well as the second-nearest-neighbor exchange between Cr–S–Crinteractions, are the main causes of the ferromagnetism of the CrSBrmonolayer.33 The former exchange interactionis mediated by the orbital hybridization between Cr interstitialsand inner Br atoms . As a result,the AFM coupling strength between the layers becomes weaker becauseof the appearance of defects.We furtherinvestigated the preferred magnetic ordering, includingthe energy difference between the lowest energy AFM and FM orderingsee 4. Foral9 However, X-raydiffraction and cross-sectional high-resolution scanning transmissionelectron microscopy measurements of our irradiated crystals  do not indicate any expansionof the lattice along the c-axis within the detectionlimit. Recent works have demonstrated the possibility of strain, hydrostaticpressure or structural phase transition in modifying the magneticproperties of CrSBr.32We note that the change of the magnetic groundstate observed afterion irradiation could in principle also be caused by lattice expansion,e.g., due to intercalation.We observe a change of the magnetic ground state of the vdWmagnetCrSBr after irradiation by non-magnetic ions. The fluence-dependentremanent magnetization of the irradiation-induced FM state existsup to a critical temperature of 110 K and adapts the magnetic easyaxis anisotropy of the AFM order before irradiation. The saturationmagnetic moment of FM magnetization is estimated to be close to themaximum magnetic moment of the constituent Cr atoms. First-principlescalculations suggest that the displacement of atoms into interstitialpositions by high-energy ions is responsible for the experimentallyobserved transition of the magnetic ground state as the interstitialsfacilitate ferromagnetic coupling between the layers of CrSBr. We note that ion implantation is a mature technology forchip fabrication. Applying proper lithography, one can create artificiallateral structures. By tuning the energy of the ions and hence thepenetration depth, it may further be possible, in a vdW heterostructure,to maximize the impact of ions for CrSBr but not the material on top.As a result, our approach may be applied to modify the magnetic propertiesof CrSBr even after fabrication of a vdW heterostructure.


Social anxiety and paranoia often co-occur and exacerbate each other. While loneliness and negative schemas contribute to the development of social anxiety and paranoia separately, their role in the development of the two symptoms co-occurring is rarely considered longitudinally. This study examined the moment-to-moment relationship between social anxiety and paranoia, as well as the effects of loneliness and negative schemas on both experiences individually and coincidingly. A total of 134 non-clinical young adults completed experience sampling assessments of momentary social anxiety, paranoia, and loneliness ten times per day for six consecutive days. Participants’ negative-self and -other schemas were assessed with the Brief Core Schema Scale. Dynamic structural equation modelling revealed a bidirectional relationship between social anxiety and paranoia across moments. Loneliness preceded increases in both symptoms in the next moment. Higher negative-self schema was associated with a stronger link from paranoia to social anxiety; whereas higher negative-other schema was associated with a stronger link from social anxiety to paranoia. Our findings support the reciprocal relationship between social anxiety and paranoia. While loneliness contributes to the development of social anxiety and paranoia, negative self and other schemas appear to modify the relationships between the two symptoms. Compared with patients without comorbidity, those with comorbid social anxiety disorder report lower quality of life, more severe depression, and a higher rate of suicide5. The level of social anxiety in schizophrenia spectrum disorders needs only be at a subclinical level to be associated with poorer functioning, both concurrently and longitudinally6. This suggests that social anxiety may contribute to poorer outcomes in individuals with psychosis, even when present at subclinical levels.Around one-fifth of individuals with schizophrenia spectrum disorders have a comorbid social anxiety disorder . This finding supports loneliness as a common psychopathological pathway to the development of both social anxiety and paranoia.Delineating the temporal dynamics between social anxiety and paranoia will not only reveal their potential bidirectional relationship, but also allow for investigations of putative underlying mechanisms. As both social anxiety and paranoia are experiences derived from the social environment, how connected one feels to the surrounding social environment could be important to the manifestation of these symptoms. Loneliness, a negative experience arising from a mismatch between perceived and actual social relationships32 and paranoia34 have suggested that both symptoms build on negative-self beliefs , which facilitate appraisals that one is vulnerable to social threats. Paranoia is suggested to be specific to negative-other beliefs 36, leading to a biased interpretation of others’ intention as hostile and malevolent. Recently, a cognitive model of social anxiety in schizophrenia highlights the role of negative-self schema37. The model posits that negative social situations activate preexisting negative self-representation, leading to the appraisal of social threats in the forms of social anxiety and paranoia. While the model does not specify the role of negative-other schema, we expect negative-other schema to contribute to a stronger tendency towards the formation of paranoia .Apart from loneliness, negative schemas  may also underlie the development of social anxiety and paranoia. Cognitive models of social anxiety38. Compared to traditional retrospective questionnaires, ESM represents these experiences with less recall bias39, which is particularly important when investigating momentary beliefs and appraisals. Importantly, ESM data provides valuable insights into the temporal dynamics between variables (i.e. cross-lagged effects) while taking into account their tendency to carry over across time (i.e. autoregressive effects)41. The autoregressive effects represent the extent to which a variable at the previous moment t-1 predicts itself at the current moment t, indicating the carry-over effect within the same variable across moments. On the other hand, the cross-lagged effects represent the extent to which a variable at the previous moment t-1 predicts change in another variable at the current moment t, indicating the spill-over effect from a variable to another variable across moments.As social anxiety, paranoia and loneliness occur naturalistically in the flow of daily life with varying intensities across hours and days, they can be reliably captured by the experience sampling method (ESM). ESM refers to repeated self-report questionnaires that record subjective experiences across moments in the flow of daily life44. The aim of the present study was threefold: First, we tested the moment-to-moment dynamics between social anxiety and paranoia. We hypothesized significant cross-lagged effects from social anxiety to paranoia and vice versa. Second, we examined the moment-to-moment dynamics between loneliness and the two symptoms. We hypothesized significant cross-lagged effects from loneliness to both social anxiety and paranoia. Third, we tested the associations of core schemas with the strength of the cross-lagged effects. We hypothesized that negative-self schema would increase the strength of the cross-lagged bi-directional effects between social anxiety and paranoia. We also hypothesized a positive association between negative-other schema and the strength of the cross-lagged effect from social anxiety to paranoia.While previous studies mainly recruited samples across a large age span, the current study focused on young adulthood (i.e. age 18–30), a life stage where people are most vulnerable to loneliness, social anxiety and paranoiaOne hundred and fifty-four participants consented and took part in the study. Twenty participants were excluded due to a past or current psychiatric diagnosis n = 18), or a subthreshold completion rate of the ESM assessment . Therefore, the final sample consisted of ESM data from 134 participants. The majority of our sample consisted of undergraduate students , while the remaining  were adults from the general population. A total of 5,800 ESM entries were entered into the Dynamic Structural Equation Modelling (DSEM) analyses . The mean duration between two consecutive ESM entries within a day was 80.18 min . The descriptive statistics of participants’ demographic information and baseline survey can be found in Table 8, or a sp > 0.050).The estimates of the fixed and random effects in Models 1 and 2 are shown in Table p > 0.050).For Model 2, as shown in Table The between-person correlations between negative schemas and DSEM parameters for Model 2 are reported in Table This study utilized ESM to repeatedly assess momentary symptoms in daily life and found that social anxiety predicted an increase in paranoia across moments and vice versa. Such reciprocal relationships were demonstrated in a sample of young adults in the absence of full-blown psychiatric disorders. These relationships did not differ between genders. Our findings showed that social anxiety and paranoia do not merely co-exist, but also dynamically interact with one another in their development and maintenance.23, our analytical approach using DSEM focused on the covariation of both symptoms in a single model (Model 1). For the first time, we offered evidence for the bidirectional relationship within the same sample, revealing comparable effect sizes of each directional path. In addition to previous conceptualization of social anxiety as an antecedent to paranoia , our results also supported it as a consequence of paranoia as shown in other studies23. Future studies may clarify the overlap of paranoid thinking with the affective, cognitive and behavioral manifestations of social anxiety, which would inform the underlying processes in both symptoms.In addition to previous longitudinal studies which considered social anxiety and paranoia separately27. We confirmed the ‘healthy’ status of our sample with a psychiatric interview; therefore, our findings reflected the relationship between social anxiety and paranoia free from the confounding effects by treatments and chronicity of the psychiatric disorders. The convergent finding from Lim et al.27 and our study support loneliness as a common psychopathological pathway to both social anxiety and paranoia. While previous studies have found that loneliness leads to heightened vigilance for social threat via a myriad of affective and social-cognitive processes30, the contributions of these processes in differentiating social anxiety from paranoia outcomes need to be ascertained in further studies.We then took a closer look at loneliness in the moment-to-moment dynamics between social anxiety and paranoia (Model 2). We found that loneliness predicted an increase in both social anxiety and paranoia, corroborating with a longitudinal study with a community sample32. The findings also supported the specificity between negative-other schema and paranoia36, where negative beliefs about others (e.g. ‘Others are harsh and bad’) would exacerbate the formation of paranoid thinking, possibly against the backdrop of social anxiety. Importantly, our findings highlighted the presence of both negative-self and -other schemas to be necessary to the maintenance of the reciprocal relationship between social anxiety and paranoia. This speculation is consistent with the finding of a recent latent profile analysis by Chau et al.10. They identified a subgroup of non-clinical young adults high on both social anxiety and paranoia, who reported more negative-self and -other schemas than subgroups high on either symptom. Future studies may examine how various constellations of negative-self- and -other schemas would shape the development of various phenotypic expressions of social anxiety and paranoia. Our findings also pave ways for future investigation of the potential between-person heterogeneity in these moment-to-moment dynamics, which may longitudinally predict the transition into social anxiety disorder, schizophrenia and their co-morbidity.As hypothesized, the level of negative-self schema was associated with a stronger relationship from paranoia to social anxiety, whereas the level of negative-other schema was associated with a stronger relationship from social anxiety to paranoia. These findings were in line with the proposed role of negative beliefs about self (e.g. ‘I am worthless and weak’) in the formation of fear of rejection and criticism implicated in social anxiety37’s unified framework for the understanding the psychopathological processes underlying social anxiety and paranoia. In particular, loneliness appears to be a situational trigger to the emergence of social anxiety and paranoia, in which their dynamics are strengthened by negative schemas. Our findings further extended Aunjitsakul et al.’s37 cognitive model with the role of negative-other schemas, which may exaggerate the appraisal of social threat in terms of harm and malevolence, which define paranoia35. Our findings shed light on the possibility of ameliorating social anxiety and paranoia via interventions that reduce loneliness46 or challenge negative-self and -other schemas (e.g. cognitive restructuring49).In sum, our findings offered support to Aunjitsakul et al.52. Although the baseline levels of these phenomena were comparable to another sample of demographically diverse non-clinical young adults tested before the outbreak of the pandemic10 (N = 2089), we could not ascertain the confounding impact of the pandemic on the expression of these phenomena in daily life36. Third, a majority of our sample were undergraduate students. It is not sure whether our results would be replicated in demographically diverse samples. Finally, we acknowledge the possibility that the dynamics between social anxiety and paranoia may also involve other unmeasured mechanisms beyond loneliness and negative core schemas, such as interpersonal trauma54. This should be investigated in future research.There are several limitations of the current study. First, our results may be specific to the current sampling frequency of ESM assessment. Despite the statistical adjustment to confine the temporal effects to one-hour windows, it is inevitable that any effects that operate at shorter or longer time windows would be missed. Second, our data collection was conducted during the COVID-19 pandemic, a period when exacerbated loneliness, social anxiety and paranoia were reportedUsing ESM, the current findings supported the reciprocal relationship between social anxiety and paranoia. Loneliness was also found to predict increases in both anxiety and paranoia across moments, suggesting that loneliness predates and may lead to the increases in both symptoms. Moreover, the strength of the dynamics between social anxiety and paranoia was associated with levels of negative-self and -other schemas. Our findings shed new light on the understanding of the dynamics between social anxiety and paranoia, which may invite replications in the clinical populations.Ethics approval for the study was granted by the Survey and Behavioral Research Ethics Committee of The Chinese University of Hong Kong (Reference no.: SBRE-19–788). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants.57. Our targeted sample size fulfilled the sample size recommendation from a recent simulation study for DSEM58.Eligible participants aged 18–30 were recruited either from the subject pool of the Introductory Psychology course or via campus recruitment. Participants with any past or current psychiatric diagnosis  and who could not read Chinese were excluded. We targeted a sample size of 130, which is comparable to previous ESM studies with non-clinical samples analyzed using the dynamics structural equational modelling (DSEM) 59). Participants without any past or current psychiatric diagnosis completed a baseline survey, and were then briefed individually on the ESM procedure.Data collection took place in June to October 2021. It happened to be after the peak of the fourth wave of the COVID-19 pandemic in Hong Kong. While face-to-face data collection was allowed by the university, territory-wide infection control measures such as social distancing and mask-wearing mandate were in place. Consented participants attended a 1-h assessment session during which they were screened with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV  installed on the participant’s mobile phone. Adopting a signal-contingent sampling design, the app prompted participants to answer the same set of items assessing momentary loneliness, social anxiety and paranoia (see Measures below) ten times a day for six consecutive days. The app displayed the items one by one in a way that the preceding item had to be answered before the next item would appear. The prompt signals were pseudo-randomized into blocks of time intervals within 13 waking hours. The starting time of the ESM assessment was tailored for each participant to maximize compliance. Consecutive ESM questionnaires were set at least 15 min apart. Each ESM questionnaire expired in 15 min. The participant completed at least one ESM questionnaire as practice under the guidance of a research worker.The ESM questionnaires were programmed into a smartphone app 62, the Patient Health Questionannire-9 (PHQ-9)63 and the Social Interaction Anxiety Scale-6/Social Phobia Scale-6 (SIAS-6/SPS-6)64. The UCLA-LS-v3 and SIAS-6/SPS-6 do not specify the timeframe of reference, whereas PHQ-9 and R-GPTS assess depressive symptoms and paranoia within two weeks and one month respectively. The Chinese versions of these measures have been validated and used in previous studies36. Negative-self and -other schemas were measured with the respective subscales of the Brief Core Schema Scale65. Its Chinese version has been used in Chau et al.10 and So et al.36. Internal consistencies of these measures ranged from 0.78 to 0.93 in this sample. Items on age, gender, educational attainment, monthly household income and employment status were also included.Participants completed retrospective questionnaires assessing levels of loneliness, paranoia, depression, and social anxiety. These included the UCLA-Loneliness Scale version 3 (UCLA-LS-v3)All ESM measures were rated on a 7-point Likert scale .66 was modified to assess momentary loneliness . These three items have been used in a previous ESM study29. In the current study, the within- and between-person reliabilities were 0.86 and 0.98 respectively.The 3-item UCLA Loneliness scale67 . It has been used in previous ESM studies 69. In the current study, the within- and between-person reliabilities were 0.84 and 0.99 respectively.Momentary social anxiety was assessed with the three items suggested in Kashdan and Steger70 . These items have been used in previous ESM studies72. The within-(0.84) and between-person (0.99) reliabilities were good in the current study.Momentary paranoia was assessed with the five items suggested by Schlier et al.73. Our hypotheses were tested with Dynamic Structural Equation Modeling (DSEM)41. DSEM allows the examination of multi-level relationships among ESM variables by decomposing the intensive longitudinal data into within- and between-person variance components using a latent person-mean approach. For the within-person components, the fixed effects of means of ESM variables (i.e. intercepts), their autoregressive effects and cross-lagged effects were simultaneously estimated in a single model. The autoregressive effects were estimated by regressing the variables at the current moment t on the same variables at the previous moment t-1, while the cross-lagged effects were estimated by regressing the variable at the current moment t on another variable at the previous moment t-1. To allow for inter-individual differences in these fixed effects, the DSEM estimated all the random effects at the between-person level, which were allowed to correlate with each other.In accordance with previous ESM studies, responses from participants who completed less than one-third of the total ESM questionnaires (i.e. 20) were excluded from the data analysis40. Within-person standardized parameters of the fixed effects74 were computed for interpretation. Estimates of all fixed effects were regarded as statistically significant if their 95% credible intervals (CrIs) did not include zero. Tests for model comparison were not conducted, as model comparison is an underdeveloped area for DSEM40.Bayesian estimation is supported in DSEM to estimate all random effects in a single model with high accuracy and computational efficiency. The default non-informative priors were used in this study. Four Markov Chain Monte Carlo (MCMC) chains with 5000 iterations each were used, with a thinning of 10. Missing data was assumed to be missing at random and handled with MCMC sampling40. Therefore, the interpretation of all parameters was in reference to the time window of one hour. A simulation study indicated that estimates of parameters are unbiased up to 80–85% of missing data58.To control for potential trends or non-stationarity of ESM data, the hour of measurement was added in the within-person level of the DSEM models as fixed effects. Unequal time spacing of the ESM data was handled by creating time grids of one hour using a discrete time filter approach using the Mplus option TINTERVALFor the first hypothesis, we fitted the bivariate multilevel first-order vector autoregressive model Model 1) with within-person cross-lagged effects between momentary social anxiety and paranoia, while controlling for their autoregressive effects (see schematic representation in Fig.  with wit


Due to the sharp increase in household waste, its separate collection is essential in order to reduce the huge amount of household waste, since it is difficult to recycle trash without separate collection. However, since it is costly and time-consuming to separate trash manually, it is crucial to develop an automatic system for separate collection using deep learning and computer vision. In this paper, we propose two Anchor-free-based Recyclable Trash Detection Networks (ARTD-Net) which can recognize overlapped multiple wastes of different types efficiently by using edgeless modules: ARTD-Net1 and ARTD-Net2. The former is an anchor-free based one-stage deep learning model which consists of three modules: centralized feature extraction, multiscale feature extraction and prediction. The centralized feature extraction module in backbone architecture focuses on extracting features around the center of the input image to improve detection accuracy. The multiscale feature extraction module provides feature maps of different scales through bottom-up and top-down pathways. The prediction module improves classification accuracy of multiple objects based on edge weights adjustments for each instance. The latter is an anchor-free based multi-stage deep learning model which can efficiently finds each of waste regions by additionally exploiting region proposal network and RoIAlign. It sequentially performs classification and regression to improve accuracy. Therefore, ARTD-Net2 is more accurate than ARTD-Net1, while ARTD-Net1 is faster than ARTD-Net2. We shall show that our proposed ARTD-Net1 and ARTD-Net2 methods achieve competitive performance in mean average precision and F1 score compared to other deep learning models. The existing datasets have several problems that do not deal with the important class of wastes produced commonly in the real world, and they also do not consider the complex arrangement of multiple wastes with different types. Moreover, most of the existing datasets have an insufficient number of images with low resolution. We shall present a new recyclables dataset which is composed of a large number of high-resolution waste images with additional essential classes. We shall show that waste detection performance is improved by providing various images with the complex arrangement of overlapped multiple wastes with different types. Recently, the increasing population has been accompanied by a vast amount of resource consumption. Therefore, separate collection is essential to reduce the huge amount of household waste by recycling trash. Since it is costly and time-consuming to separate trash manually, it is very important to develop an automatic system for separate collection using deep learning and computer vision ,2,3,4. IIn this paper, we shall propose two Anchor-free based Recyclable Trash Detection Networks (ARTD-Net) for accurately recognizing overlapped multiple wastes of different types by using edgeless module: ARTD-Net1 and ARTD-Net2. The former is an anchor-free-based one-stage deep learning model which consists of three modules: centralized feature extraction, multiscale feature extraction and prediction. The centralized feature extraction module is used as the backbone in the model, focusing on extracting features around the center of the input image. The extracted feature maps are passed on to the multiscale feature extraction module for further processing. The multiscale feature extraction module generates five feature maps of different sizes through the combination of bottom-up and top-down pathways. The prediction module improves detection accuracy by exploiting feature maps of different sizes and efficiently finding small-sized objects using anchor-free detection, and adjusting edge weights for each instance to improve classification accuracy of multiple objects. The latter is an anchor-free based multi-stage deep learning model which can efficiently finds each of waste regions by additionally exploiting Region Proposal Network and RoIAlign. It sequentially performs classification and regression to improve accuracy. Additionally, we build a new recyclables dataset which consists of a large number of high-resolution images of waste. Our dataset provides various images with complex arrangements, such as overlapped wastes of different types, and comprises additional essential classes.We propose two Anchor-free based Recyclable Trash Detection Networks (ARTD-Net) which can recognize overlapped multiple wastes of different types efficiently by using edgeless module: ARTD-Net1 and ARTD-Net2. ARTD-Net2 is more accurate than ARTD-Net1, while ARTD-Net1 is faster than ARTD-Net2. We shall show that our proposed ARTD-Net1 and ARTD-Net2 achieve competitive performance in mean average precision and F1 score compared to other deep learning models.We propose a background weight adjustment block in the centralized feature extraction module which improves the detection accuracy by focusing on around the center of the input image based on centralized weights adjustments.We propose an object instance separation block in the prediction module which improves the classification accuracy of multiple objects based on edge weights adjustments for each instance.We present a multi-stage model for ARTD-Net which efficiently finds each of waste regions by using Region Proposal Network and RoIAlign.We contribute a new large scale recyclables dataset which comprises various additional essential classes, and include various high resolution images with the complex arrangement, i.e., with several overlapped wastes with different types.The contributions of our research are summarized as follows:The remainder of this paper comprises four sections. In TrashNet dataset  is a colThe TACO dataset was created to detect waste dumped in the sea. This dataset consists of 1500 images with 4,784 annotations that are labeled in 60 subcategories that belong to 28 super-categories . MJU-WasObject detection methods can generally be divided into two main methods: one-stage and multi-stage. One-stage method performs classification and regression at the same time. This results in faster processing times compared to the multi-stage method. We analyze YOLO series ,24,25,26YOLO is a real-time object detector designed for high-speed processing. It finds the location and class using a single network pass. YOLO divides the input image into multiple grids, where the grid cell closest to the center of the object is responsible for detecting the object. Each grid cell predicts the corresponding bounding boxes, along with their confidence scores and conditional class probabilities. YOLO has a limitation where the performance is somewhat reduced for small objects due to the small difference in IoU values. To address this limitation, improved models such as YOLOx, YOLOF, YOLOv5 and YOLOv6 have been proposed.SSD detects objects of various sizes by dividing the input image into grids of different sizes across six feature maps. It is constructed using six additional convolution layers on the fifth convolution layer of VGG-16.FSAF is a one-stage model based on RetinaNet . They imFCOS is a one-stage model which detects objects in a per-pixel prediction fashion. It is based on an anchor-free method and reduces training time by eliminating the need for complex calculations associated with anchor boxes such as calculating overlapping during training. They utilize centerness to decrease the impact of bounding boxes generated far from the center of an object. Centerness reduces the influence of predicted values at locations far from the center of the object using the center of the bounding box, right-bottom, left-top and corner pairs.CornerNet predicts object bounding boxes using a keypoint pair instead of anchors, where the keypoints represent the top-left and top-right corners of the target object. Instead of using anchor boxes, CornerNet estimates these keypoints based on feature points. CentripetalNet is a keypoint-based object detector which utilizes centripetal shift to generate a keypoint pair representing the corners of the same object.Faster R-CNN is an object detection model which is developed to improve the slow speed of R-CNN caused by selective search. The selective search is the slowest part of R-CNN. It is computed in the CPU. To improve this, Faster R-CNN introduces RPN that can perform computations on the GPU. RPN takes a feature map as input which is obtained from the previous convolutional neural network layer. A 256-dimension vector is obtained using a sliding window on the received feature map. At this time, the anchor to be used as the window being set in advance. In Faster R-CNN, nine anchors with various width, height, ratio, and size are used. The class and location of the object are calculated through two layers using the 256-dimensional vector obtained in this way.Cascade R-CNN proposes a method to address two issues that occur with the increase in IoU thresholds. With an increase in IoU thresholds, the number of positive samples decreases exponentially, which can lead to overfitting during training. In addition, if there is a difference between IoU thresholds used during training and those used at inference, it can result in decreased accuracy. Cascade R-CNN is comprised of a sequence of detectors with different IoU thresholds set. Detectors are connected sequentially and use the output from the previous step as input for the next step. As a result, each detector has the positive set of examples of equivalent size to solve the overfitting problem. It shows that performance is improved through the process of gradual training using the proposals of the learned detectors at low IoU.The Double-Head method utilizes the commonly used two head structures for classification and localization tasks in R-CNN based detectors. They found that the fully connected head is better for classification while the convolution head is better for localization. To leverage the strengths of both structures, they proposed the Double-Head method which combines a fully connected head for classification and a convolution head for bounding box regression.Sparse R-CNN is a purely sparse method for object detection. It utilizes a fixed sparse set of learned object proposals provided to the object recognition head for classification and location. Sparse R-CNN directly outputs final predictions without a non-maximum suppression post-procedure and demonstrates accuracy performance on par with well-established detector baselines on the COCO dataset.DetectoRS proposes a backbone design utilizing a see and think mechanism with improvements at two levels: macro and micro. At the macro level, the method involves building Recursive Feature Pyramid by adding feedback connections to the existing FPN. At the micro level, they propose Switchable Atrous Convolution which utilizes a switch function to collect features extracted at various atrous rates.In this section, we shall present ARTD-Net which is an anchor-free based deep learning model for detecting multiple wastes with various types. We propose two versions of ARTD-Net: ARTD-Net1 and ARTD-Net2. ARTD-Net1 is a one-stage model which performs classification and regression at the same time, while ARTD-Net2 a multi-stage model which sequentially performs classification and regression. ARTD-Net1 is faster than ARTD-Net2, but ARTD-Net2 is more accurate than ARTD-Net1.As shown in i of a backbone, and H and W the height and width of the feature map, respectively, and C the number of class. Let The centralized feature extraction module is obtained by adding BWAB (Background Weight Adjustment Block) to RetinaNet. BWAB exploits a new scheme which efficiently extracts features around the center of the input image while reducing the edge weights around the border of the image. BWAB consists of two components: Background Weight Adjustment kernel and the convolution layer. Let H and W are the height and width of feature maps, respectively. s is the downsampling ratio of the feature maps.Then, the function of Background Weight Adjustment kernel is defined as follows:The multiscale feature extraction module generates feature maps of different scales through convolution layer based on feature map received from the backbone. The multiscale feature extraction module consists of two pathways: bottom-up and top-down. The bottom-up pathway reduces the size of the feature map by half through downsampling, and the top-down pathway doubles the size of the feature map through upsampling. The previous FPN produces three feature maps using only the top-down pathway as shown in i denotes a layer level of prediction module, and There are five prediction modules each of which performs classification and regression simultaneously for object instance. They efficiently find the small-sized wastes by using anchor-free detection, and improve detection accuracy by exploiting the feature maps of different size. Prediction module consists of two blocks: OISB (Object Instance Separation Block) and ODB (Object Detection Block). OISB reduces the interference between adjacent objects by reducing the edge weight of each object through instance separation kernel. Let Then, Instance Separation Kernel is defined as follows: Feature layer receives the feature map from multiscale feature extraction module, and then instance separation layer adjusts the edge weight of each object using the instance separation kernel in order to improve detection accuracy for overlapped multiple wastes of different types. The feature map is sent to ODB after applying the convolution layer. This phase is illustrated in p be the predicted probability of the class for the object instance. Given p, CLF is defined as follows: ODB detects multiple wastes with various sizes through anchor-free based object detection. Since the size of the anchor is fixed in the existing anchor-based models, it is difficult to detect the object smaller than the size of the anchor. ODB in our model is trained using the ground truth bounding box for the object instead of the fixed anchor most approximate to the bounding box in order to efficiently find the small objects whose bounding boxes are much smaller than the anchor candidate. ODB finds the loss value for our model by using Class Loss Function (CLF) and Box Regression Loss Function (BRLF). CLF calculates a class loss for the object instance based on the focal loss function. It decreases the loss for the high probability class while increasing for the low probability class. Let BRLF is a bounding box regression function which calculates a loss by finding the IoU between the ground truth bounding box and the prediction bounding box.Let L at layer l in the prediction module is defined as follows: Then, the loss function Then, the final total loss ARTD-Net2 is a multi-stage model which improves the accuracy of waste detection compared to ARTD-Net1 by attaching Region Proposal Network in the prediction module and additionally exploiting recursive feature extraction scheme as shown in The centralized feature extraction module is used as the backbone in the multi-stage model. The feature map in each layer is sent to recursive multiscale feature extraction module connected to the layer as shown in Recursive multiscale feature extraction module recursively performs the process of extracting features by multiscale feature extraction module to minimize feature loss. It improves accuracy by extracting important but missing features through the feedback network which sends the result of the first step to the input of the second step. The top-down pathway of multiscale feature extraction module is implemented as the unrolled iteration, and can be repeated n times.The prediction module consists of two stages: the first stage has Region Proposal Network (RPN) and RoIAlign, and the second stage OISB and ODB. It is designed to combine anchor-based RPN and anchor-free ODB. RPN improves detection accuracy by increasing the number of anchor candidates. RoIAlign converts the results of RPN, which have different sizes, into a fixed-sized feature map in order to input them into the fully connected layer. OISB reduces the interference between nearby objects by reducing the edge weight of each object through instance separation kernel. ODB improves accuracy by detecting small objects which are not detected in RPN through anchor-free-based object detection.In this section, we shall describe about the performance evaluation for ARTD-Net1 and ARTD-Net2, respectively. Our experimental environment consists of eight GPUS, each NVIDIA A100 40 GB, and one AMD EPYC Processor with 92 single-cores and 1.7TB RAM. Our model was implemented using the Pytorch framework and MMDetection  with PytWe used F1 score and mean Average Precision (mAP) as evaluation metrics to measure the performance of model. The F1 score is the harmonic mean of precision and recall, and is particularly useful for evaluating model performance on datasets with class imbalance. mAP is evaluation metric in object detection tasks. It is calculated as the mean of the Average Precision (AP) scores for each class.This section is organized as follows: We build our recyclables dataset by focusing on the overlapped multiple wastes of different types with the complex arrangement. It comprises a total of 50,183 images of various resolutions and 110,759 annotation data, and has the largest number of annotations among trash datasets. The resolution of the image is 1280 × 720 on average and 4032 × 2268 at maximum. Several types of waste, up to eight, are placed in one image in order to increase the batch complexity over the previous dataset. Recyclable waste objects are classified into ten classes: paper, paper pack, paper cup, can, bottle, PET, plastic, vinyl, cap, label. Our dataset format follows COCO. The cap class has the maximum number of 8462 data and the paper pack class has the second largest number of 7705 data. Cap and label classes have the largest number of annotations as they are obtained from PET or bottles.As shown in We make use of our dataset in the previous section for the experiments. Experiments are conducted for F1 Score and mAP using the same backbone, ResNet-50. As shown in First, we investigate the performance of centralized feature extraction module with BWAB in the backbone. It shows that adding BWAB into backbone improves the accuracy by reducing the edge weights around the border for the image. As shown in Second, we check the performance of prediction module. It shows that adding OISB into prediction module improves the accuracy by reducing the edge weight of each object. our model with OISB has higher F1 score and mAP than that without OISB, showing better performance. Our model has F1 score of 81.688 and mAP of 0.506. It has higher F1 score of 1.765 and mAP of 0.038 than the model without both blocks.Our model with both of BWAB and OISB has the highest score for F1 score and mAP.We compare the F1 score and mAP of ARTD-Net1 with the other one-stage models. As shown in We make use of our dataset for the experiments. Experiments are conducted for F1 Score and mAP using the same backbone, ResNet-101. As shown in First, we investigate the performance of centralized feature extraction module with BWAB in the backbone of ARTD-Net2. It shows that adding BWAB into backbone improves the accuracy by reducing the edge weights around the border for the image. As shown in Second, we check the performance of prediction module of ARTD-Net2. It shows that adding OISB into prediction module improves the accuracy by reducing the edge weight of each object. Our model with OISB has a higher F1 score and mAP than that without OISB, showing better performance. Our model has an F1 score of 85.14 and mAP of 0.581. It has a higher F1 score of 1.27 and mAP of 0.041 than the model without both blocks.For all the cases, F1 Score and mAP achieve the highest score when using both BWAB and OISB.We compare the F1 score and mAP of ARTD-Net2 with the other multi-stage models. As shown in We analyze the two experimental results to compare the performance of ARTD-Net1 and ARTD-Net2: Confusion Matrix, Processing Time and Accuracy.First, we compare the accuracy of each class by using the confusion matrix after training our model with dataset. Second, we compare the performance of ARTD-Net1 and ARTD-Net2 through accuracy and processing time of our model. As shown in Finally, we show that ARTD-Net1 is faster than ARTD-Net2 but ARTD-Net2 is more accurate than ARTD-Net1.In this paper, we have proposed two anchor-free-based Recyclable Trash Detection Networks (ARTD-Net) which can recognize overlapped multiple wastes of different types efficiently by using edgeless module: ARTD-Net1 and ARTD-Net2. The former is an anchor-free based one-stage deep learning model which consists of three modules: centralized feature extraction, multiscale feature extraction and prediction. The centralized feature extraction module in the backbone architecture is obtained by adding BWAB to RetinaNet. BWAB exploits a new scheme which efficiently extracts features around the center of the input image while reducing the edge weights around the border of the image. The multiscale feature extraction module provides feature maps of different scales through bottom-up and top-down pathways. The prediction module consists of two blocks: OISB and ODB. OISB reduces the interference between nearby objects by reducing the edge weight of each object through instance separation kernel. ODB improves accuracy by detecting small objects which are not detected in RPN through anchor-free-based object detection. The latter is an anchor-free based multi-stage deep learning model which can efficiently finds each of waste regions by additionally exploiting Region Proposal Network and RoIAlign. It sequentially performs classification and regression to improve accuracy. We have presented a new recyclables dataset which is composed of a large number of high-resolution waste images with additional essential classes. We demonstrated that waste detection performance is improved by providing various images with the complex arrangement of overlapped multiple wastes with different types. The performance of ARTD-Net is confirmed through various experiments. ARTD-Net1 with BWAB and OISB achieves F1 score and mAP of 82.849 and 0.518, respectively, which is 2.926 and 0.05 higher than ARTD-Net1 without BWAB and OISB. Among the one-stage models, ARTD-Net1 with ResNext-101 achieves the highest F1 score and mAP of 85.594 and 0.596, respectively. ARTD-Net2 with BWAB and OISB achieves F1 score and mAP of 86.089 and 0.603, respectively, which is 2.219 and 0.063 higher than ARTD-Net2 without BWAB and OISB. Among the multi-stage models, ARTD-Net2 with ResNext-101 achieves the highest F1 score and mAP of 88.351 and 0.617, respectively. Finally, we demonstrated that ARTD-Net1 is faster than ARTD-Net2 but ARTD-Net2 is more accurate than ARTD-Net1.For future works, we shall work on the details of ARTD-Net such as the number of blocks for each module and hyperparameters, and continue to collect data under more various lighting and background conditions to use our dataset for various works such as field diagnostics.


Breast cancer is the most frequent cancer in women in Africa and contributes to premature death and poor quality of life. This study aimed to determine the validity, reliability, and psychometric properties of the Swahili version of EORTC QLQ-BR45 among women with breast cancer in Tanzania. A cross-sectional study design with non-probability convenience sampling was employed. Data were collected in two tertiary hospitals and one national cancer institute; 414 participants completed the EORTC-QLQ-C-30 and EORTC-QLQ-BR45. The reliability of QLQ-BR45 was measured using Cronbach’s alpha and McDonald’s Omega coefficients. The factor structure of EORTC QLQ-BR45 was assessed using confirmatory factor analysis. The internal consistencies for the five dimensions were all above 0.7 indicating satisfaction, except for systemic therapy side effects with a marginal value of 0.594 and significant correlations between the dimensions of QLQ-C30 and BR45. The final model fit well to the data, with the comparative fit index = 0.953, Tucker–Lewis index = 0.947, root mean square error of approximation = 0.041 , and standardized root mean square residual = 0.072. In conclusion, the QLQ BR45 Swahili version displayed good reliability, validity, and psychometric properties and can be used in Swahili-speaking Sub-Saharan countries.  Cancer in Sub-Saharan Africa (SSA) affects many of its one billion inhabitants. It is among the three leading causes of premature death (30–69 years) in almost all constituent countries . Breast In Tanzania, a national cancer registration system coordinated by the Ministry of Health has been developed; existing data indicate increasing incidence rates, with 40,000 new cancer cases annually . Female Specialized cancer care in Tanzania is limited to five urban tertiary hospitals with limited services: two are located in Dar es Salaam, and one in Mwanza, Kilimanjaro, and Mbeya, respectively . Howeverwww.qol.eortc.org (accessed on 2 July 2023). There are also different cancer-specific burden QoL questionnaires depending on the type of cancer to be studied. The first breast-cancer-specific questionnaire was EORTC-QLQ–BR23 developed in 1996 [Quality of life (QoL) among breast cancer patients in Tanzania is low probably because of the limited cancer care centres. Previous investigators reported that cancer patients in Tanzania displayed low QoL in physical, social, and role functioning . Moreove in 1996 . The evo in 1996 . In 2020 in 1996 . The EOR in 1996 ,18,19. FThe Kiswahili language, here called Swahili, is the national language in Tanzania and is one of the most widely spoken languages in Sub-Saharan Africa. It has more than 200 million speakers and is among the 10 most spoken languages in the world. Swahili is approved as an official language by the African Union , and theThis was a descriptive cross-sectional design study where a non-convenience sampling was employed. Data were collected from November 2020 to August 2021 at three out of five cancer care centres in Tanzania, namely Kilimanjaro Christian Medical Centre Cancer Care Centre in Moshi , Ocean Road Cancer Institute in Dar es Salaam , and Bugando Medical Centre Cancer Care Centre in Mwanza . The COVID-19 pandemic had some negative impact on the data collection due to a decreased number of patients who were visiting healthcare facilities, although there was no restriction in Tanzania regarding travelling within the country. However, during the data collection, the research assistants and patients took the recommended COVID-19 precautions such as wearing masks and keeping distances according to the Tanzania government and WHO protocols.The study population consisted of adult women with breast cancer treated at one of the three cancer care centres in Tanzania, namely Kilimanjaro Christian Medical Centre Cancer Care Centre in Moshi, Ocean Road Cancer Institute in Dar es Salaam, and Bugando Medical Centre Cancer Care Centre in Mwanza. Women aged 18–70 years, diagnosed with breast cancer, who could speak the Swahili language were asked to participate in the present study. Terminal patients and those who could not understand or speak Swahili were excluded. Convenience sampling was used to obtain the required sample across the three study settings after obtaining the ethical clearance certificate and permission from the respective hospitals’ executive directors. The researchers, thereafter, introduced the study to the hospitals’ matron who authorized access to patients at the wards and outpatient clinics. Women with breast cancer who visited the study setting for treatment were approached and informed about the study by the researcher and/or trained research assistants . Those who agreed to participate in the study were asked to sign an informed consent form to confirm their voluntary participation in the study. The research assistants assisted the participants by reading the questions and filling in the participants’ answers. This procedure was maintained throughout the course of the study. The reason why the research assistants read and filled in the questionnaires together with the participants was that there are still people in the country who are illiterate; therefore, to ensure the quality of the data, the researcher filled in the forms together with the respondents.p* = 0.5 [The sample size was estimated using the sample size for a proportion formula, i.e., a planned value of p* = 0.5 . Since mp* = 0.5 . In this study, the Swahili versions of the EORTC-QLQ-C30 and the EORTC QLQ-BR45 were used. Permission to translate and use the tool was granted by EORTC headquarters in 2020. The EORTC QLQ-BR45 comprises 45 questions distributed into four functional scales  and five symptom scales/items . These tools use a four-point scale from 1 = not at all, to 4 = very much, and a scoring scale of 0–100, with a high score indicating better functioning and severity for high symptoms/item scale ,29. DataThe EORTC QLQ-BR45 was translated according to the EORTC translation unit standards . The forThe EORTC QLQ-BR45 Swahili version was piloted on a group of 10 breast cancer patients at Kilimanjaro Christian Medical Centre Cancer Care Centre who were native speakers of the Swahili language. According to the EORTC translation unit standards , the pilFor the current study, two approaches of psychometric analyses were considered: item response theory (IRT) and classic test theory (CTT) . The CTTTo further evaluate the EORTC QLQ-BR45 based on IRT, a Rasch analysis using a partial credit model was performed. Item scores were assessed using inlier-sensitive mean square (infit MnSq) and outlier-sensitive (outfit) MnSq with values within 0.5 and 1.5 as satisfactory . DiffereThe convergent validity of the EORTC QLQ-BR45 was examined by computing the Spearman’s rank correlation coefficients between the subscales of the EORTC QLQ-BR45 and EORTC-QLQ-C30. Known group validity was conducted to examine whether the EORTC QLQ-BR45 could distinguish between age (<50 vs. ≥50 years), WHO performance status scale  . Most of the functioning and symptoms subscales scores of Cronbach’s alpha for QLQ-C-30 were above the cut-off point > 0.70 except for systemic therapy side effects (α = 0.59) as displayed in The results of the CFA models for the EORTC QLQ-BR45 are displayed in Additionally, the factor covariances among the EORTC QLQ-BR45 were all significant and ranged from −0.177  to 0.796 (between systemic therapy side effects and endocrine therapy symptoms). The Cronbach’s alpha internal consistencies for the five dimensions of the EORTC QLQ-BR45 were all above 0.7, except for systemic therapy side effects, which had a marginal value of 0.594 . The resThere were significant correlations between the dimensions/domains of the EORTC QLQ-BR45 and EORTC QLQ-C30 . Howeverp-value for body image (p = 0.048).The results of the known group validity of the EORTC QLQ-BR45 are shown in The study was conducted to meet the international standards of measuring the quality of life among breast cancer patients in Tanzania using the standardized EORTC QLQ-BR45 Swahili version by assessing its validity and reliability. This study shows that the Swahili version of EORTC QLQ-BR45 has good reliability, validity, and psychometric properties. To ensure the quality of the EORTC QLQ-BR45 Swahili version is culturally sensitive, the English version was translated into Swahili according to EORTC translation guidelines . First, The EORTC QLQ-BR45 English and Swahili version instruments validation approach utilized a similar QoL questionnaire with 45 items. The EORTC QLQ-BR45 Swahili version was used for the first time, it was validated, and psychometric properties were computed. Our findings are in congruence with the original version of EORTC QLQ-BR45 English version, as factorial loading was significant . MoreoveMoreover, there were significant correlations between dimensions of the Swahili version of EORTC QLQ-BR45 and EORTC QLQ-C30, though sexual functioning and enjoyment were not statistically significantly correlated with emotional, cognitive, or social functioning as well as fatigue, nausea, pain, or global health status. The findings could probably be attributed to African countries’ culture, including Tanzania, whereby people are shy or prohibited from discussing sexual matters openly, and thus a minimal response to the questions related to sexuality occurred, which affects the statistical test. This concurs with , though The results of known group validity of the EORTC QLQ-BR45 demonstrated body image and sexual functioning significantly differentiated among age groups with a high burden in old age. Young women aged <50 years had better functioning compared to their counterparts. This demonstrated that the Swahili version is valid and reliable as most theories discriminate the performance and severity of diseases among age groups, with poor performance in advanced diseases and a high burden among older ages.The CFA was computed to assess the EORTC QLQ-BR45 Swahili version’s structure. Several models were examined to test scale structure via the CFA. The final model demonstrated acceptable fit indices of the EORTC QLQ-BR45 Swahili version structure with CFI and TLI >0.90, similar to other studies ,19,35. TThe global quality of life score of the EORCT QLQ-BR45 and QLQ C-30 Swahili version among breast cancer patients in Tanzania was low, being 63 (SD = 20). This could probably be due to a late diagnosis . MoreoveThis study utilized data from cancer centres in two (2) zonal referral hospitals and one (1) national referral hospital in Tanzania. Therefore, the sample can be considered representative of the entire country. This ensures the reliability of the data collected in this research project. Moreover, the sample size was large enough to compute the psychometric properties of the EORTC QLQ-BR45 Swahili version. Confirmatory factor analysis demonstrated strong scale structure and overall internal consistency for functioning and the symptoms scale meets the international standards set for measuring QoL among breast cancer patients. In this context, the researcher recommends the EORTC QLQ-BR45 Swahili version be used to measure QoL among breast cancer patients globally as an evaluation of treatment outcomes and strategies to improve survival rates. The inadequate response rate for sexual functioning and sexual enjoyment limited the evaluation of the correlation between dimensions and some EORTC QLQ C-30 functioning and symptoms domains, demanding advocacy for sexual education both to patients and healthcare providers to assist patients, as both breast cancer disease and treatment affect sexual desire. Sexual dysfunction among these groups can affect the quality of life in addition to the cancer disease and treatment, thus this needs to be taken care of.The Swahili version of EORTC QLQ-BR45 has good psychometric properties, is reliable and valid, and can be used to determine breast cancer patients’ QoL in Swahili-speaking Sub-Saharan African countries. In this study, we believe using the EORTC QLQ-BR45 Swahili version will simplify data collection in this region and measure QoL among breast cancer patients accurately will assist in improving the QoL of patients with breast cancer. Further studies that measure the QoL during breast cancer treatment are recommended to confirm the results of the present study.


Biomineralization of fish otoliths is regulated by macromolecules,such as proteins, whose presence is crucial for the functionalityand properties of these mineralized structures. Special regulatoryeffects are exerted by intrinsically disordered proteins, such asthe polyanionic Starmaker-like protein from medaka, a homolog of zebrafishStarmaker. In this study, we employed a set of bioinspired mineralizationexperiments with a single diffusion system to investigate the effectof the Starmaker-like protein on calcium carbonate biominerals withregards to the prior exposition of the protein to calcium or carbonateions. Interestingly, the bioinspired minerals grown in the presenceof the Starmaker-like protein in calcium- or carbonate-type experimentsdiffer significantly in terms of morphology and protein distributionwithin the crystals. Our deeper analysis shows that the Starmaker-likeprotein action is a result of the environmental conditions to whichit is exposed. These findings may be of special interest in the areasof biomineralization process pathways and biomaterial sciences. Solublematrix proteins, such as Starmaker, OMM-64, or Sparc,12 are direct biomineral growth regulators, while insoluble proteinsin particular form a gelatinous meshwork on which calcium carbonateis deposited.14 The endolymph of the inner ear,where otoliths are formed, is rich in both mineral phase constituentsand macromolecules; thus, the mineralization site is naturally crowded.16Otoliths, or ear stones, are primary componentsof the hearingand gravity perception system in fish.18 To understandthe action of these individuates in the processes of biologicallycontrolled mineralization, so-called in vitro diffusion systems areused.21 These systems involve the slow delivery of calciumcarbonate components to the mineralization site where the proteinunder study is present. One of the most common systems for studyingcrystal growth is that involving the slow diffusion of carbonate ionsinto a buffered aqueous solution of calcium chloride with the protein.22 This system, despite its many advantages, isremarkable for its simplicity and the fact that it does not allowfor mineralization in a highly crowded environment. A good alternativeto the slow diffusion system is the counterdiffusion system (CDS)or its variant, the single diffusion system.25 In essence,counter and single diffusion systems rely on mineralization in a gelenclosed in a U-pipe or pipe, respectively. The gel-like environmentprovides a highly condensed matrix that resembles the environmentof the natural mineralization process.26 Moreover, the ion concentration at the mineralization site is controlledby diffusion and gradually changes as the experiment progresses. Varioustypes of macromolecules and low-molecular weight compounds can beembedded in gels.28 CDS was used in the investigationof soluble matrix proteins from Balanophyllia europaea and Leptopsammia pruvoti scleractinian corals.28Recently, research on otolithmineralization has become more widespread,and more is now known about the proteome of ear stones and the roleof individual proteins in their formation.6 Their unstructured nature allows them to adapt to localconditions and to specifically buffer the mineralization site by bindingions and slowly delivering them to the growing biocrystal. Many proteinsdirectly involved in the biomineralization process of fish otolithsare functional homologs, such as the Starmaker-like (Stm-l) proteinfrom medaka (Oryzias latipes) and Starmakerfrom zebrafish (Danio rerio).29 Stm-l has been identified as a biomineralization-relatedprotein by in situ hybridization screening of inner ear-expressedgenes and is considered a Stm homolog based on similarity at the genomiclevel.30 The intrinsically disordered Stm-lprotein is elongated, possesses a very pliable structure, and is ableto undergo major conformational changes in the presence of structuringagents. As shown by previous in vitro bioinspired mineralization studiesusing the slow diffusion method, Stm-l influences the size, shape,and morphology of calcium carbonate crystals.29 Moreover, Stm-l promotes calcium carbonate nucleation. In the growingcrystal, it is distributed in an annular manner with a protein-enrichedring. Interestingly, incorporation of Stm-l causes shrinkage of thecalcium carbonate crystal lattice.31Typically, soluble matrix proteins in biomineralization aredisorderedand possess a highly negative, uncompensated charge.23 prompted us to investigate the protein in twoseparate experimental setups composed of calcium-type and carbonate-typeexperiments, where Stm-l was embedded in a gel supplied with eithercalcium or carbonate ions, respectively. The results obtained by thesingle diffusion system show that the exposure of Stm-l to the givenions influences the morphology of the crystals and the distributionof Stm-l in the crystals differently based on the type of experiment.Stm-l was located in the central part of the crystal when incubatedwith calcium ions, while it was peripherally distributed when exposedto carbonate ions. The thermogravimetric and X-ray diffraction (XRD)data derived from two types of Stm-l bioinspired mineralization assaysindicate that the action of Stm-l is directly associated with thecomposition of its environment and reflected in the biocrystal crystallinelevel. Our experimental setup and results shed light on new possibilitiesin biomineralization process research and provide an interesting approachfor developing new bioinspired materials.A subsequent study of the calcium carbonate bioinspired mineralizationprocess in the presence of Stm-l using the single diffusion systemis reported in this work. The preliminary results of Stm-l bioinspiredmineralization activity in a gel environment examined by CDSAll buffers were prepared at 24 °C usinghigh-purity Milli-Q water. Buffer A included 20 mM TRIS and 150 mMNaCl with a pH of 7.5. TopVision Low Melting Point Agarose was obtainedfrom Thermo Fisher Scientific . All other reagentswere obtained from Carl Roth GmbH + Co. KG .29 Briefly, Stm-l was overexpressed in BL21(DE3)pLysS E. coli cells  andpurified using fractionation with ammonium sulfate, size-exclusionchromatography, and anion exchange chromatography. Purified proteinwas desalted to buffer A and stored at −80 °C. Prior toall experiments, buffer A was changed to deionized water using AmiconUltra-0.5 mL 10 K centrifugal filters (Merck). The protein concentrationwas determined spectrophotometrically at 280 nm.A recombinant, nontagged Stm-lprotein was obtained as previously described.31 The labeled protein(Stm-l/AF488) concentration and the degree of labeling (DOL) weredetermined spectrophotometrically by measuring absorbance at 280 and494 nm. The calculated DOL was 1.9.Protein labelingusing Alexa Fluor 488 NHS ester dye  was performed as ina previous study.Figure S1). All sections were placed in the newest Eppendorf tubescontaining 1 mL of hot (60 °C) Milli-Q water. Crystals were extractedby dissolving the gel and collected by centrifugation. The obtainedprecipitates were washed three times with hot Milli-Q water and air-driedat room temperature. Control hydrogel experiments were performed similarlybut without the protein. Reference calcium carbonate crystals wereobtained by directly mixing 0.1 M solutions of calcium chloride andsodium carbonate and incubating them at room temperature for 3 days.Then, crystals were collected by centrifugation and washed and air-driedat room temperature.Crystallizationexperiments were conducted at room temperature using a single diffusionbioinspired mineralization test in a glass column 75 mm in lengthand 6 mm in diameter. Stm-l was preincubated with calcium chloride or with sodium carbonate (for the carbonate-typesetup) at room temperature for 30 min. Agarose hydrogel was preparedby dissolving agarose in Milli-Q water that was heated to 70 °C.Then, the protein–ion solution was mixed with melted agaroseto obtain 0.4 mL of solution containing a final protein concentrationof 5, 50, or 200 μg/mL, an ion concentration of 0.1 M, and anagarose concentration of 0.5% (w/v). The gel was poured into the columnand maintained at 4 °C for gelation. After that, 0.2 mL of melted0.5% (w/v) agarose was poured into the column on top of the protein/ion-embeddedgel and maintained at 4 °C for gelation. Finally, 1 mL of 0.1M counterion solution was poured on top of the gel. The column wassealed to avoid evaporation and maintained at room temperature for3 days. After that, the protein/ion-embedded gel was removed fromthe column and cut into three equal sections numbered I, II, and III,where I indicates the section of the gel closest to the counterionreservoir, II is the section in the center, and III is the sectionfarthest from the counterion reservoir  equipped with a confocal DM 2500 Leica optical microscope, a thermoelectricallycooled CCD as a detector, and a diode laser operating at 830 nm. Eachspectral profile is an average of five spectra.Raman spectra in the 100–1500 cmThe structuralcharacterization of the calcium carbonate crystals was performed withscanning electron microscopy (SEM) using a Philips XL-20 scanningmicroscope  at an accelerating voltage of25.0 kV. All crystals had previously been coated with a carbon layer.Thermogravimetric analyses (TGA) were performed on a Mettler-ToledoTGA/DSC 1. Samples were heated from 30 to 1000 °C at a rate of20 °C/min and then maintained at 1000 °C for 10 min. Theanalysis was performed under air (80 mL/min).α radiation wasemployed in all experiments. The resulting powder diffraction patternwas interpreted with respect to mineral content using the Bruker DIFFRACsuite software package EVA in comparison to the ICDD PDF libraryof reference patterns. Quantitative analysis of the diffraction datawas performed using the Bruker DIFFRACsuite software package TOPAS.This involved iterative modeling of the full diffraction pattern byRietveld refinement, minimizing the differences between the modeledand measured patterns. The results are normalized to a total of 100%of the phases used for refinement.For mineralogicalanalysis, the samples were gently ground withethanol in an agate mortar and then transferred to a single crystalSi waver, on which they were measured following evaporation of theethanol. The mineralogical composition was analyzed according to Bragg’slaw using a Bruker D8 Advance ECO powder diffractometer with a 1 kWX-ray tube, a sample holder, a θ–θ goniometer,and a LYNXEYE XE-T detector, scanning at 0.02° per s from 3 to65° 2θ. Ni-filtered Cu K32 or Icy platform version 1.9.5.0.33Images of the fluorescentlylabeled Stm-l/AF488 protein were acquired using the Leica TCS SP8confocal system  equipped with an HC PL APO CS 40x/0.85DRY objective lens. Crystals were covered by immersion oil  before measurement. The fluorescence of Stm-l/AF488was excited with 488 nm light (OPSL 488 laser at an intensity of 0.50%)and detected in the 500–652 nm range. Images were acquiredusing LAS X software version 3.5.2 and processed using ImageJ version1.4834 In our preliminary studies, usingCDS, we observed different morphologies of the obtained calcium carbonatecrystals depending on the order of gel and protein molecule exposureto a given ion solution ;23 therefore, we decided to investigate this phenomenon usingthe single diffusion method, which seems to be a better research toolin this particular case. Stm-l is known to regulate the nucleationand formation of calcium carbonate crystals in slow-diffusion bioinspiredmineralization experiments;29 nonetheless,our deeper investigation revealed new features of Stm-l bioinspiredmineralization activity in single diffusion system experiments.Agarose hydrogels are routinelyused in crystal growth studiesbecause they are considered relatively inert gel matrices. This iswhy they are used to elucidate the physical effects of the gel  on calcium carbonate crystalgrowth. However, the chemical functionality of agarose gels can beadded back by, for example, the introduction of soluble ions or smallmolecules.23 In turn, theregulated ion accessibility at the mineralization site is reflectedin the crystal morphology. Calcium carbonate crystals grown in supersaturatedconditions show characteristic hopper-like morphologies.35 Moreover, in this case, hopper-like crystalsgrow rapidly and, in turn, can incorporate strong gels that are resistantto expansion. Additionally, hopper-like crystals grow by homogeneousnucleation.36 On the other hand, crystalsgrowing in unsaturated conditions, where ion flux  is limited, result in rosette- and otoconia-like morphologies,respectively, causing the ion flux to decrease.35 The nucleation in this case occurs in a heterogeneous way.Notably, nucleation in biologically regulated mineralization is alsogoverned by the heterogeneous pathway in the majority of cases.37The mineralization of calcium carbonate in hydrogels is primarilydependent on the hydrogel, additives, and diffusion of ionic constituentsthroughout the gel pores.38 To mimic these conditions, we useda single diffusion system and considered two mineralization scenariosin which Stm-l is present in a calcium or carbonate ion-rich environment.The Stm-l protein and its homologs arecrucial regulatory macromoleculesin the formation of fish otoliths. The natural environment in whichStm-l takes action is the highly crowded endolymph of the fish innerear.Figure S2A,B and The SEM images revealedthat depending on the single diffusionbioinspired mineralization test type used, different morphologiesof the obtained calcium carbonate precipitates were observed 1A,B. In The morphologies of the biogenic calcium carbonatecrystals obtainedin the calcium-type single diffusion experiments are determined primarilyby the protein concentration and ion concentration related to itstransportation in the hydrogel (as the distance from the reservoir).In the calcium-type experiment, where the carbonate ion concentrationis variable, hopper-like crystals are formed in control conditions,where no Stm-l is present. This observation also suggests a homogeneousnucleation pathway and rapid growth of the crystals in this case.In turn, rosette-like and otoconia-like crystals are formed in thepresence of variable Stm-l concentrations and distances from the carbonateion reservoir. Comparing both conditions, we conclude that in calcium-typeexperiments, the presence of Stm-l in the gel regulates the availabilityof calcium ions for the incoming carbonate ions. This is particularlyevidenced by the adapted morphologies of the crystals depending ontheir distance from the carbonate ion source and the protein concentration.Thus, Stm-l influences the overall ion saturation at the nucleationsite in the calcium-type single diffusion experiment. The tendencyto grow rosette- and otoconia-like crystals is indicative of the contributionof Stm-l to the nucleation process, shifting it from homogeneous toheterogeneous, compared to controls with unmoderated crystals. Surprisingly,the bioinspired mineralization activity of Stm-l is reduced and alteredin the case of the carbonate-type experiment. All of the obtainedcrystals, both those grown as controls and in the range of proteinconcentrations and calcium ion reservoir distances, show similar morphologies.Nonetheless, biogenic crystals have distinctive surface textures andare notably larger in size than inorganic crystals. The overall morphologyresembles hopper-like crystals in all cases. Based on that, we concludethat in the carbonate-type experiment, the ion regulation activityof Stm-l is limited once Stm-l is incubated in an environment richin carbonate ions. The limitation of the Stm-l biomineralization activityis most likely dictated by the repulsion of negatively charged carbonateions and polyanionic Stm-l at the mineralization site.z-direction of representative crystalsfrom section II grown without (control) and in the presence of anincreasing concentration of Stm-l. We did not observe fluorescencein the control group of crystals precipitated with AF488 dye [z-position present in Confocallaser scanning microscopy (CLSM) was used to determinethe localization of Stm-l in calcite crystals from both types of singlediffusion bioinspired mineralization tests. 31 In turn, theperipheral distribution of the protein in the crystals obtained asa result of the carbonate-type test influences their rough morphologies. In the presence ofcalcium ions, in a calcium-type experiment, Stm-l shows its characteristicbioinspired mineralization activity, which is involved in nucleationand inhibition of crystal growth.29 Obtainedresults suggest the presence of dense liquid droplets at the earlystages of calcium carbonate crystallization in the presence of Stm-lthat function as heterogeneous nuclei for the growing minerals.39 In contrast, in the carbonate-type experiment,the adsorption or specific interaction of Stm-l on the surfaces ofcalcium carbonate crystals is mainly caused by its lack of bioinspiredmineralization activity. This may be explained by (a) the repulsiveinteraction between Stm-l and carbonate ions and (b) the zeta potentialof the nonbiogenic calcite surface. We hypothesize that carbonateions, which are initially dominant over Stm-l in the environment,create nonbiogenic crystal nuclei without Stm-l being embedded (a).While the growing crystal depletes the availability of carbonate ions,the negatively charged Stm-l could bind to the surface of alreadyformed nonbionic calcite with a positive zeta potential and providenew crystallization sites on its surface by binding to it (b).41The localization of the protein in the crystalexplains its morphology.In the case of the samples from the calcium-type test, the centraldistribution may indicate the nucleating-agent properties of the protein,as has already been shown.2+ is much higher than that of the other samples (includingStm-l CO32–), indicating that in thistest type, these noncrystalline substances are much better incorporatedthan in the other experiment sets.The TG curves for the obtained samples are given in 42 where the effects of gel strength and crystal-growth kinetics wereinvestigated for controlling the incorporation of agarose gel networksinto crystals43 and the calcium carbonatecrystals were obtained in the CDS with the presence of agar (whosecomponent is agarose). In a given range, the weight losses for thesamples with the protein were higher than those for the control, whichmay indicate the synergistic binding of the protein and the hydrogel;the presence of the protein strengthens the gel matrices. It was shownthat if the gel network is strong enough to resist the crystallizationpressure, the gel fibers will be pressed into the growing crystal.42 In the next temperature range, there is mostlikely a loss of the remaining hydrogel and protein localized on thesurface of the crystal as the weight losses of the samples with theprotein are much different from those of the controls and the reference.However, only temperatures above 500 °C allow the protein todecompose from the central part of the crystal because the weightloss for the Stm-l Ca2+ sample is significantly higherin this temperature range compared to those of all other samples.The protein distribution in the crystals also explainsthe obtainedTGA results. In the first temperature range, all the bioinspired mineralizationsamples have higher weight losses compared to the reference. Thisis most likely due to the hydrogel decomposition , as was shown in a previous study,32– also contain small amounts of the aragonitepolymorph. The relative quantities of these polymorphs modeled withthe Rietveld refinement44 are given in 31 the XRD patterns displaya peak shift in the calcite peaks relative to the position of thevaterite peaks lattice planes, indicates a wider spacing d of theselattice planes .At the same time, an apparent increase in the lattice parameters ofthe crystals formed in the carbonate-type experiment suggests an increasedinclusion of the hydrogel under these conditions. We assume that thisis caused by faster crystal growth (the gel strength remains the samein both sets). The fast growth rates were determined to favor thegrowth of calcite around the fibers as a result of the reduced timefor mass transport to the mineral–organic interface. The crystalproperties and morphologies obtained in our experiments indicate thatthe agarose concentration used in our study allows for the formationof a strong hydrogel network. This agarose network was able to resistthe crystallization pressure exerted by the crystal, forcing growtharound the fibers and enhancing hydrogel incorporation.46 Yang et al.35 proposed that the formation of hopper-like calcites isgoverned by the incorporation of hydrogel networks. These observationsare in good agreement with our results for control crystals, wherethe presence of hydrogel caused the appearance of similar hopper-likecrystals and caused the calcite XRD peaks to shift toward the small-anglesides, increasing the interplanar distance d andexpanding the host lattice. The presence of Stm-l probably compensatesfor the effect of agarose by interacting with the inorganic part ofthe crystal, similar to the phenomenon described earlier for crystalsobtained from the slow diffusion method.31 Another explanation is that Stm-l simply drives the polymer fromthe crystal network by decreasing the crystal growth rate and/or gelstrength. Thus far, it is difficult to determine which scenario ismore reasonable in the presented case. Although native Stm-l couldinteract differently with the organic scaffold and mineral latticein otoliths, we believe that the knowledge gained on the distributionof Stm-l and hydrogel in calcite crystals and the determined propertiesof the protein molecules may have direct implications for understandingthe role of proteins in otolith biomineralization. Importantly, thepossibility of including soluble organic and biological matter inthe hydrogel-like environment may provide opportunities to investigatediverse bioinspired mineralization pathways and open up promisingstrategies for designing new biomaterials with improved specific propertiesfor medical uses.The XRD data confirmed the existence of differencesbetween thecalcium carbonate samples obtained from the different single diffusionbioinspired mineralization tests. The XRD patterns showed a noticeablerelative shift in the positions of the diffraction peaks for calciteto lower 2θ values in all experimental samples relative to thereference sample, especially for those obtained in the carbonate-typetest sets. Although the peak shift was normalized to the peak positionsof vaterite (and any peak shift in vaterite would thus have gone unnoticed),this suggests that agarose and/or the protein were incorporated intothe calcite structure during crystallization, which is in good agreementwith our previous findings for Stm-l bioinspired mineralization insolution.49 Our previous biochemical characterizationof Stm-l together with high-resolution X-ray powder diffraction ofStm-I/calcite obtained by the slow diffusion method indicated thatthe pliable conformation of Stm-l facilitates its interaction withinorganic constituents of bioinspired minerals and incorporation intocalcite crystallites, causing shrinkage of the crystal lattice.31 In this work, we investigated the effect of protein on calcium carbonatemineralization in agarose hydrogels combined with the effect of gelexposure on calcium and carbonate ions. Our results revealed thatthe action of Stm-l is directly associated with the composition ofits hydrogel-like environment. In the calcium-type single diffusionexperiments, where the concentration of carbonate ions is limited,Stm-l influences the overall ion saturation at the nucleation siteand shifts the nucleation process from homogeneous to heterogeneous,resulting in the growth of rosette- and otoconia-like crystals. Onthe other hand, in the case of the carbonate-type experiment, theion regulation activity of Stm-l is limited, most likely due to therepulsion of negatively charged carbonate ions and polyanionic Stm-lat the mineralization site, as well as due to the fast crystal growththat impeded the proteins into the surroundings in the beginning.Despite the fact that the location of the protein differs in the differentsingle diffusion experiment types, both the hydrogel and Stm-l areincorporated into the calcite structure during the crystallizationprocess, regardless of the experiment type. Calcium carbonate protectsthe Stm-l inside the crystal by shifting the burning-off temperatureof the protein determined in TGA experiments toward extremely highvalues. The incorporation of agarose, manifested by expanding thehost lattice, is forced by the growth rate and gel strength. As thisphenomenon is more clearly visible in the crystals formed in the carbonate-typeexperiment, we assumed that it is caused by faster crystal growthin this environment. Interestingly, Stm-l compensates for the effectof agarose by interacting with the mineral and shrinking the latticeparameters and/or by reducing the crystal growth rate, gel strength,or both, hindering the polymer network from penetrating the crystallattice.It was already shown that the inclusionof organic matter withinthe crystalline lattice affects the morphological, optical, and mechanicalproperties of the biomineral.The method allowed us to investigate protein activitydependenton the conditions of the environment. To our knowledge, most bioinspiredmineralization experiments use methods where the protein, hydrogel,or other additive is first exposed to calcium ions, followed by thecarbonate ions, causing the precipitation of calcium carbonate withcharacteristic morphology. In our research, we want to pay attentionto the possible alternative results of the obtained studies when wereverse the order of exposure of the additives to the involved ions,as is possible in the natural environment. We believe that the knowledgegained on the distribution of Stm-l in calcitic crystals obtainedin two different experimental setups may have direct implicationsin understanding the role of additives in biomineralization, as wellas nucleation pathways. Furthermore, biomineralization is an inspirationfor the rational design of functionalized materials. Observation ofthe Stm-l behavior depending on the environment in which it is locatedconstitutes an important aspect in research on bioinspired materials.